# Introduction to Data Analysis using R

R is a statistical programming language whose popularity is quickly overtaking SPSS and other “traditional” point-and-click software packages (Muenchen, 2015). But why would anyone use a programming language, instead of point-and-click applications, for data analysis? An important reason is that data analysis rarely consists of simply running a statistical test. Instead, many small steps, such as cleaning and visualizing data, are usually repeated many times, and computers are much faster at doing repetitive tasks than humans are. Using a point-and-click interface for these “data cleaning” operations is laborious and unnecessarily slow:

“[T]he process of tidying my data took me around 10 minutes per participant as I would do it all manually through Excel. Even for a moderate sample size, this starts to take up a large chunk of time that could be spent doing other things like writing or having a beer” (Bartlett, 2016).

A programmed analysis would seamlessly apply the tidying steps to every participant in the blink of an eye, and would itself constitute an exact script of what operations were applied to the data, making it easier to repeat the steps later.

Learning to use a programming language for data analysis reduces human labor and saves time that could be better spent doing more important (or fun) things. In this post, I introduce the R programming language, and motivate its use in Psychological science. The introduction is aimed toward students and researchers with no programming experience, but is suitable for anyone with an interest in learning the basics of R.

# The R project for statistical computing

“R is a free software environment for statistical computing and graphics.” (R Core Team, 2016)

Great, but what does that mean? R is a programming language that is designed and used mainly in the statistics, data science, and scientific communities. R has “become the de-facto standard for writing statistical software among statisticians and has made substantial inroads in the social and behavioural sciences” (Fox, 2010). This means that if we use R, we’ll be in good company (and that company will likely be even better and numerous in the future, see (Muenchen, 2015)).

To understand what R is, and is not, it may be helpful to begin by contrasting R to its most common alternative, SPSS. Many psychologists are familiar with SPSS, which has a graphical user interface (GUI), allowing the user to look at the two-dimensional data table on screen, and click through various drop-down menus to conduct analyses on the data. In contrast, R is an object oriented programming language. Data is loaded into R as a “variable”, meaning that in order to view it, the user has to print it on the screen. The power of this approach is that the data is an object in a programming environment, and only your imagination limits what functions you can apply to the data. R also has no GUI to navigate with the mouse; instead, users interact with the data by typing commands.

SPSS is expensive to use: Universities have to pay real money to make it available to students and researchers. R and its supporting applications, on the other hand, are completely free—meaning that both users and developers have easier access to it. R is an open source software package, which means that many cutting edge statistical methods are more quickly implemented in R than SPSS. This is apparent, for example, in the recent uprising of Bayesian methods for data analysis (e.g. Buerkner, 2016).

Further, SPSS’s facilities for cleaning, organizing, formatting, and transforming data are limited—and not very user friendly, although this is a subjective judgment—so users often resort to a spreadsheet program (Microsoft Excel, say) for data manipulation. R has excellent capacities for all steps in the analysis pipeline, including data manipulation, and therefore the analysis never has to spread across multiple applications. You can imagine how the possibility for mistakes, and time needed, is reduced when the data file(s) doesn’t need to be juggled between applications. Switching between applications, and repeatedly clicking through drop-down menus means that, for any small change, the human using the computer must re-do every step of the analysis. With R, you can simply re-use your analysis script and just import different data to it.

These considerations lead to contrasting the two different workflows in Figure 1. Workflow 1 uses a programming language, such as R. It is difficult to learn, but beginners generally get started with real analysis in an hour or so. The payoff for the initial difficulty is great: The workflow is reproducible (users can save scripts and show their friends exactly what they did to create those beautiful violin plots); the workflow is flexible (want to do everything just the way you did it, but instead do the plots for males instead of females? Easy!); and most importantly, repetitive, boring, but important work is delegated to a computer.

The final point requires some reflecting; after all, computer programs all work on computers, so it sounds like a tautology. But what I mean is that repetitive tasks can be wrapped in a simple function (these are usually already available—you don’t have to create your own functions) which then performs the tasks as many times as you would like to. Many tasks in the data cleaning stage, for example, are fairly boring and repetitive (calculating summary statistics, aggregating data, combining spreadsheets or columns across spreadsheets), but less so when one uses a programming language.

Workflow 2, on the other hand, is easy to learn because there are few well-defined and systematic parts to it—everything is improvised on a task-by-task basis and done manually by copy-pasting, pointing-and-clicking and dragging and dropping. “Clean and organize” the data in Excel. “Analyze” in SPSS. In the optimal case where the data is perfectly aligned to the format that SPSS expects, you can get a p-value in less than a minute (excluding SPSS start-up time, which is quickly approaching infinity) by clicking through the drop-down menus. That is truly great, if that is all you want. But that’s rarely all that we want, and data is rarely in SPSS’s required format.

Workflow 2 is not reproducible (that is, it may be very difficult if not impossible to exactly retrace your steps through an analysis), so although you may know roughly that you “did an ANOVA”, you may not remember which cases were included, what data was used, how it was transformed, etc. Workflow 2 is not flexible: You’ve just done a statistical test on data from Experiment 1? Great! Can you now do it for Experiment 2, but log-transform the RTs? Sure, but then you would have to restart from the Excel step, and redo all that pointing and clicking. This leads to Workflow 2 requiring the human to do too much work, and spend time on the analysis that could be better spent “doing other things like writing or having a beer” (Bartlett, 2016).

So, what is R? It is a programming language especially suited for data analysis. It allows you to program (more on this below!) your analyses instead of pointing and clicking through menus. The point here is not that you can’t do analysis with a point-and-click SPSS style software package. You can, and you can do a pretty damn good job with it. The point is that you can work less and be more productive if you’re willing to spend some initial time and effort learning Workflow 1 instead of the common Workflow 2. And that requires getting started with R.

## Getting started with R: From 0 to R in 100 seconds

If you haven’t already, go ahead and download R, and start it up on your computer. Like most programming languages, R is best understood through its console—the interface that lets you interact with the language.

After opening R on your computer, you should see a similar window on your computer. The console allows us to type input, have R evaluate it, and return output. Just like a fancy calculator. Here, our first input was assigning (R uses the left arrow, <-, for assignment) all the integers from 0 to 100 to a variable called numbers. Computer code can often be read from right to left; the first one here would say “integers 0 through to 100, assign to numbers”. We then calculated the mean of those numbers by using R’s built in function, mean(). Everything interesting in R is done by using functions: There are functions for drawing figures, transforming data, running statistical tests, and much, much more.

Here’s another example, this time we’ll create some heights data for kids and adults (in centimeters) and conduct a two-sample t-test (every line that begins with a “#>” is R’s output):

That’s it, a t-test in R in a hundred seconds! Note, c() stands for “combine”, so kids is now a numeric vector (collection of numbers) with 5 elements. The t-test results are printed in R’s console, and are straightforward to interpret.

At its most basic, data analysis in R consists of importing data to R, and then running functions to visualize and model the data. R has powerful functions for covering the entire process going from Raw Data to Communicating Results (or Word Processor) in Figure 1. That is, users don’t need to switch between applications at various steps of the analysis workflow. Users simply type in code, let R evaluate it, and receive output. As you can imagine, a full analysis from raw data to a report (or table of summary statistics, or whatever your goal is) may involve lots of small steps—transforming variables in the data, plotting, calculating summaries, modeling and testing—which are often done iteratively. Recognizing that there may be many steps involved, we realize that we better save our work so that we can investigate and redo it later, if needed. Therefore for each analysis, we should create a text file containing all those steps, which could then be run repeatedly with minor tweaks, if required.

To create these text files, or “R scripts”, we need a text editor. All computers have a text editor pre-installed, but programming is often easier if you use an integrated development environment (IDE), which has a text editor and console all in one place (often with additional capacities.) The best IDE for R, by far, is RStudio. Go ahead and download RStudio, and then start it. At this point you can close the other R console on your computer, because RStudio has the console available for you.

## Getting started with RStudio

Figure 3 shows the main view of RStudio. There are four rectangular panels, each with a different purpose. The bottom left panel is the R console. We can type input in the console (on the empty line that begins with a “>”) and hit return to execute the code and obtain output. But a more efficient approach is to type the code into a script file, using the text editor panel, known as the source panel, in the top left corner. Here, we have a t-test-kids-grownups.R script open, which consists of three lines of code. You can write this script on your own computer by going to File -> New File -> R Script in RStudio, and then typing in the code you see in Figure 3. You can execute each line by hitting Control + Return, on Windows computers, or Command + Return on OS X computers. Scripts like this constitute the exact documentation of what you did in your analysis, and as you can imagine, are pretty important.

The two other panels are for viewing things, not so much for interacting with the data. Top right is the Environment panel, showing the variables that you have saved in R. That is, when you assign something into a variable (kids <- c(100, 98, 89, 111, 101)), that variable (kids) is visible in the Environment panel, along with its type (num for numeric), size (1:5, for 5), and contents (100, 98, 89, 111, 101). Finally, bottom right is the Viewer panel, where we can view plots, browse files on the computer, and do various other things.

With this knowledge in mind, let’s begin with a couple easy things. Don’t worry, we’ll get to actual data soon enough, once we have the absolute basics covered. I’ll show some code and evaluate it in R to show its output too. You can, and should, type in the commands yourself to help you understand what they do (type each line in an R script and execute the line by pressing Cmd + Enter. Save your work every now and then.)

Here’s how to create variables in R (try to figure out what’s saved in each variable):

And here’s how to print those variable’s contents on the screen. (I’ll provide a comment for each line, comments begin with a # and are not evaluated by R. That is, comments are read by humans only.)

Transforming data is easy: R automatically applies operations to vectors of (variables containing multiple) numbers, if needed. Let’s create z-scores of kids heights.

I hope you followed along. You should now have a bunch of variables in your R Environment. If you typed all those lines into an R script, you can now execute them again, or modify them and then re-run the script, line-by-line. You can also execute the whole script at once by clicking “Run”, at the top of the screen. Congratulations, you’ve just programmed your first computer program!

## User contributed packages

One of the best things about R is that it has a large user base, and lots of user contributed packages, which make using R easier. Packages are simply bundles of functions, and will enhance your R experience quite a bit. Whatever you want to do, there’s probably an R package for that. Here, we will install and load (make available in the current session) the tidyverse package (Wickham, 2016), which is designed for making tidying data easier.

It’s important that you use the tidyverse package if you want to follow along with this tutorial. All of the tasks covered here are possible without it, but the functions from tidyverse make the tasks easier, and certainly easier to learn.

# Using R with data

Let’s import some data to R. We’ll use example data from Chapter 4 of the Intensive Longitudinal Methods book (Bolger & Laurenceau, 2013). The data set is freely available on the book’s website. If you would like to follow along, please donwload the data set, and place it in a folder (unpack the .zip file). Then, use RStudio’s Viewer panel, and its Files tab, to navigate to the directory on your computer that has the data set, and set it as the working directory by clicking “More”, then “Set As Working Directory”.

Setting the working directory properly is extremely important, because it’s the only way R knows where to look for files on your computer. If you try to load files that are not in the working directory, you need to use the full path to the file. But if your working directory is properly set, you can just use the filename. The file is called “time.csv”, and we load it into a variable called d using the read_csv() function. (csv stands for comma separated values, a common plain text format for storing data.) You’ll want to type all these functions to an R script, so create a new R script and make sure you are typing the commands in the Source panel, not the Console panel. If you set your working directory correctly, once you save the R script file, it will be saved in the directory right next to the “time.csv” file.

d is now a data frame (sometimes called a “tibble”, because why not), whose rows are observations, and columns the variables associated with those observations.

This data contains simulated daily intimacy reports of 50 individuals, who reported their intimacy every evening, for 16 days. Half of these simulated participants were in a treatment group, and the other half in a control group. To print the first few rows of the data frame to screen, simply type its name:

The first column, id is a variable that specifies the id number who that observation belongs to. int means that the data in this column are integers. time indicates the day of the observation, and the authors coded the first day at 0 (this will make intercepts in regression models easier to interpret.) time01 is just time but recoded so that 1 is at the end of the study. dbl means that the values are floating point numbers. intimacy is the reported intimacy, and treatment indicates whether the person was in the control (0) or treatment (1) group. The first row of this output also tells us that there are 800 rows in total in this data set, and 5 variables (columns). Each row is also numbered in the output (leftmost “column”), but those values are not in the data.

## Data types

It’s important to verify that your variables (columns) are imported into R in the appropriate format. For example, you would not like to import time recorded in days as a character vector, nor would you like to import a character vector (country names, for example) as a numeric variable. Almost always, R (more specifically, read_csv()) automatically uses correct formats, which you can verify by looking at the row between the column names and the values.

There are five basic data types: int for integers, num (or dbl) for floating point numbers (1.12345…), chr for characters (also known as “strings”), factor (sometimes abbreviated as fctr) for categorical variables that have character labels (factors can be ordered if required), and logical (abbreviated as logi) for logical variables: TRUE or FALSE. Here’s a little data frame that illustrates the basic variable types in action:

Here we are also introduced a very special value, NA. NA means that there is no value, and we should always pay special attention to data that has NAs, because it may indicate that some important data is missing. This sample data explicitly tells us that we don’t know whether this person likes matlab or not, because the variable is NA. OK, let’s get back to the daily intimacy reports data.

## Quick overview of data

We can now use the variables in the data frame d and compute summaries just as we did above with the kids’ and adults’ heights. A useful operation might be to ask for a quick summary of each variable (column) in the data set:

To get a single variable (column) from the data frame, we call it with the $ operator (“gimme”, for asking R to give you variables from within a data frame). To get all the intimacy values, we could just call d$intimacy. But we better not, because that would print out all 800 intimacy values into the console. We can pass those values to functions instead:

If you would like to see the first six values of a variable, you can use the head() function:

head() works on data frames as well, and you can use an optional number argument to specify how many first values you’d like to see returned:

### A look at R’s functions

Generally, this is how R functions work, you name the function, and specify arguments to the function inside the parentheses. Some of these arguments may be data or other input (d, above), and some of them change what the argument does and how (2, above). To know what arguments you can give to a function, you can just type the function’s name in the console with a question mark prepended to it:

Importantly, calling the help page reveals that functions’ arguments are named. That is, arguments are of the form X = Y, where X is the name of the argument, and Y is the value you would like to set it to. If you look at the help page of head() (?head), you’ll see that it takes two arguments, x which should be an object (like our data frame d, (if you don’t know what “object” means in this context, don’t worry—nobody does)), and n, which is the number of elements you’d like to see returned. You don’t always have to type in the X = Y part for every argument, because R can match the arguments based on their position (whether they are the first, second, etc. argument in the parentheses). We can confirm this by typing out the full form of the previous call head(d, 2), but this time, naming the arguments:

Now that you know how R’s functions work, you can find out how to do almost anything by typing into a search engine: “How to do almost anything in R”. The internet (and books, of course) is full of helpful tutorials (see Resources section, below) but you will need to know these basics about functions in order to follow those tutorials.

## Creating new variables

Creating new variables is also easy. Let’s create a new variable that is the square root of the reported intimacy (because why not), by using the sqrt() function and assigning the values to a new variable (column) within our data frame:

Recall that sqrt(d\$intimacy) will take the square root of every 800 values of the vector of intimacy values, and return a vector of 800 squared values. There’s no need to do this individually for each value.

We can also create variables using conditional logic, which is useful for creating verbal labels for numeric variables, for example. Let’s create a verbal label for each of the treatment groups:

We created a new variable, Group in d, that is “Control” if the treatment variable on that row is 0, and “Treatment” otherwise.

Remember our discussion of data types above? d now contains integer, double, and character variables. Make sure you can identify these in the output, above.

## Aggregating

Let’s focus on aggregating the data across individuals, and plotting the average time trends of intimacy, for the treatment and control groups.

In R, aggregating is easiest if you think of it as calculating summaries for “groups” in the data (and collapsing the data across other variables). “Groups” doesn’t refer to experimental groups (although it can), but instead any arbitrary groupings of your data based on variables in it, so the groups can be based on multiple things, like time points and individuals, or time points and experimental groups.

Here, our groups are the two treatment groups and 16 time points, and we would like to obtain the mean for each group at each time point by collapsing across individuals

The above code summarized our data frame d by calculating the mean intimacy for the groups specified by group_by(). We did this by first creating a data frame that is d, but is grouped on Group and time, and then summarizing those groups by taking the mean intimacy for each of them. This is what we got:

A mean intimacy value for both groups, at each time point.

## Plotting

We can now easily plot these data, for each individual, and each group. Let’s begin by plotting just the treatment and control groups’ mean intimacy ratings:

For this plot, we used the ggplot() function, which takes as input a data frame (we used d_groups from above), and a set of aesthetic specifications (aes(), we mapped time to the x axis, intimacy to the y axis, and color to the different treatment Groups in the data). We then added a geometric object to display these data (geom_line() for a line.)

To illustrate how to add other geometric objects to display the data, let’s add some points to the graph:

We can easily do the same plot for every individual (a panel plot, but let’s drop the points for now):

The code is exactly the same, but now we used the non-aggregated raw data d, and added an extra function that wraps each id’s data into their own little subplot (facet_wrap(); remember, if you don’t know what a function does, look at the help page, i.e. ?facet_wrap). ggplot() is an extremely powerful function that allows you to do very complex and informative graphs with systematic, short and neat code. For example, we may add a linear trend (linear regression line) to each person’s panel. This time, let’s only look at the individuals in the experimental group, by using the filter() command (see below):

## Data manipulation

We already encountered an example of manipulating data, when we aggregated intimacy over some groups (experimental groups and time points). Other common operations are, for example, trimming the data based on some criteria. All operations that drop observations are conceptualized as subsetting, and can be done using the filter() command. Above, we filtered the data such that we plotted the data for the treatment group only. As another example, we can get the first week’s data (time is less than 7, that is, days 0-6), for the control group only, by specifying these logical operations in the filter() function

Try re-running the above line with small changes to the logical operations. Note that the two logical operations are combined with the AND command (&), you can also use OR (|). Try to imagine what replacing AND with OR would do in the above line of code. Then try and see what it does.

### A quick detour to details

At this point it is useful to remind that computers do exactly what you ask them to do, nothing less, nothing more. So for instance, pay attention to capital letters, symbols, and parentheses. The following three lines are faulty, try to figure out why:

Why does this data frame have zero rows?

Error? What’s the problem?

Error? What’s the problem?

(Answers: 1. Group is either “Control” or “Treatment”, not “control” or “treatment”. 2. Extra parenthesis at the end. 3. == is not the same as =, the double == is a logical comparison operator, asking if two things are the same, the single = is an assignment operator.)

Let’s move on. What if we’d like to detect extreme values? For example, let’s ask if there are people in the data who show extreme overall levels of intimacy (what if somebody feels too much intimacy!). How can we do that? Let’s start thinking like programmers and break every problem into the exact steps required to answer the problem:

1. Calculate the mean intimacy for everybody
2. Plot the mean intimacy values (because always, always visualize your data)
3. Remove everybody whose mean intimacy is over 2 standard deviations above the overall mean intimacy (over-intimate people?) (note that this is a terrible exclusion criteria here, and done for illustration purposes only)

As before, we’ll group the data by person, and calculate the mean (which we’ll call int).

We now have everybody’s mean intimacy in a neat and tidy data frame. We could, for example, arrange the data such that we see the extreme values:

Nothing makes as much sense as a histogram:

It doesn’t look like anyone’s mean intimacy value is “off the charts”. Finally, let’s apply our artificial exclusion criteria: Drop everybody whose mean intimacy is 2 standard deviations above the overall mean:

Then we could proceed to exclude these participants (don’t do this with real data!), by first joining the d_grouped data frame, which has the exclusion information, with the full data frame d

and then removing all rows where exclude is TRUE. We use the filter() command, and take only the rows where exclude is FALSE. So we want our logical operator for filtering rows to be “not-exclude”. “not”, in R language, is !:

I saved the included people in a new data set called d2, because I don’t actually want to remove those people, but just illustrated how to do this. We could also in some situations imagine applying the exclusion criteria to individual observations, instead of individual participants. This would be as easy as (think why):

### Selecting variables in data

After these artificial examples of removing extreme values (or people) from data, we have a couple of extra variables in our data frame d that we would like to remove, because it’s good to work with clean data. Removing, and more generally selecting variables (columns) in data frames is most easily done with the select() function. Let’s select() all variables in d except the squared intimacy (sqrt_int), average intimacy (int) and exclusion (exclude) variables (that is, let’s drop those three columns from the data frame):

Using select(), we can keep variables by naming them, or drop them by using -. If no variables are named for keeping, but some are dropped, all unnamed variables are kept, as in this example.

## Regression

Let’s do an example linear regression by focusing on one participant’s data. The first step then is to create a subset containing only one person’s data. For instance, we may ask a subset of d that consists of all rows where id is 30, by typing

Linear regression is available using the lm() function, and R’s own formula syntax:

Generally, for regression in R, you’d specify the formula as outcome ~ predictors. If you have multiple predictors, you combine them with addition (“+”): outcome ~ IV1 + IV2. Interactions are specified with multiplication (“*“): outcome ~ IV1 * IV2 (which automatically includes the main effects of IV1 and IV2; to get an interaction only, use”:" outcome ~ IV1:IV2). We also specified that for the regression, we’d like to use data in the d_sub data frame, which contains only person 30’s data.

Summary of a fitted model is easily obtained:

Visualizing the model fit is also easy. We’ll use the same code as for the figures above, but also add points (geom_point()), and a linear regression line with a 95% “Confidence” Ribbon (geom_smooth(method="lm")).

Pretty cool, right? And there you have it. We’ve used R to do a sample of common data cleaning and visualization operations, and fitted a couple of regression models. Of course, we’ve only scratched the surface, and below I provide a short list of resources for learning more about R.

# Conclusion

Programming your statistical analyses leads to a flexible, reproducible and time-saving workflow, in comparison to more traditional point-and-click focused applications. R is probably the best programming language around for applied statistics, because it has a large user base and many user-contributed packages that make your life easier. While it may take an hour or so to get acquainted with R, after initial difficulty it is easy to use, and provides a fast and reliable platform for data wrangling, visualization, modeling, and statistical testing.

Finally, learning to code is not about having a superhuman memory for function names, but instead it is about developing a programmer’s mindset: Think your problem through and decompose it to small chunks, then ask a computer to do those chunks for you. Do that a couple of times and you will magically have memorized, as a byproduct, the names of a few common functions. You learn to code not by reading and memorizing a tutorial, but by writing it out, examining the output, changing the input and figuring out what changed in the output. Even better, you’ll learn the most once you use code to examine your own data, data that you know and care about. Hopefully, you’ll be now able to begin doing just that.

## Resources

The web is full of fantastic R resources, so here’s a sample of some materials I think would useful to beginning R users.

### Introduction to R

• Data Camp’s Introduction to R is a free online course on R.

• Code School’s R Course is an interactive web tutorial for R beginners.

• YaRrr! The Pirate’s Guide to R is a free e-book, with accompanying YouTube lectures and witty writing (“it turns out that pirates were programming in R well before the earliest known advent of computers.”) YaRrr! is also an R package that helps you get started with some pretty cool R stuff (Phillips, 2016). Recommended!

• The Personality Project’s Guide to R (Revelle, 2016b) is a great collection of introductory (and more advanced) R materials especially for Psychologists. The site’s author also maintains a popular and very useful R package called psych (Revelle, 2016a). Check it out!

• Google Developers’ YouTube Crash Course to R is a collection of short videos. The first 11 videos are an excellent introduction to working with RStudio and R’s data types, and programming in general.

• Quick-R is a helpful collection of R materials.

### Data wrangling

These websites explain how to “wrangle” data with R.

• R for Data Science (Wickham & Grolemund, 2016) is the definitive source on using R with real data for efficient data analysis. It starts off easy (and is suitable for beginners) but covers nearly everything in a data-analysis workflow apart from modeling.

• Introduction to dplyr explains how to use the dplyr package (Wickham & Francois, 2016) to wrangle data.

• Data Processing Workflow is a good resource on how to use common packages for data manipulation (Wickham, 2016), but the example data may not be especially helpful.

### Statistical modeling and testing

R provides many excellent packages for modeling data, my absolute favorite is the brms package (Buerkner, 2016) for bayesian regression modeling.

# References

Bartlett, J. (2016, November 22). Tidying and analysing response time data using r. Statistics and substance use. Retrieved November 23, 2016, from https://statsandsubstances.wordpress.com/2016/11/22/tidying-and-analysing-response-time-data-using-r/

Bolger, N., & Laurenceau, J.-P. (2013). Intensive longitudinal methods: An introduction to diary and experience sampling research. Guilford Press. Retrieved from http://www.intensivelongitudinal.com/

Buerkner, P.-C. (2016). Brms: Bayesian regression models using stan. Retrieved from http://CRAN.R-project.org/package=brms

Fox, J. (2010). Introduction to statistical computing in r. Retrieved November 23, 2016, from http://socserv.socsci.mcmaster.ca/jfox/Courses/R-course/index.html

Muenchen, R., A. (2015). The popularity of data analysis software. R4stats.com. Retrieved November 22, 2016, from http://r4stats.com/articles/popularity/

Phillips, N. (2016). Yarrr: A companion to the e-book “YaRrr!: The pirate’s guide to r”. Retrieved from https://CRAN.R-project.org/package=yarrr

R Core Team. (2016). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/

Revelle, W. (2016a). Psych: Procedures for psychological, psychometric, and personality research. Evanston, Illinois: Northwestern University. Retrieved from https://CRAN.R-project.org/package=psych

Revelle, W. (2016b). The personality project’s guide to r. Retrieved November 22, 2016, from http://personality-project.org/r/

Wickham, H., & Francois, R. (2016). Dplyr: A grammar of data manipulation. Retrieved from https://CRAN.R-project.org/package=dplyr

### Matti Vuorre

Matti Vuorre is a PhD Student at Columbia University in New York City. He studies cognitive psychology and neuroscience, and focuses on understanding the mechanisms underlying humans' metacognitive capacities.

# Structural equation modeling: What is it, what does it have in common with hippie music, and why does it eat cake to get rid of measurement error?

Do you want a statistics tool that is powerful; easy to learn; allows you to model complex data structures; combines the test, analysis of variance,  and multiple regression; and puts even more on top? Here it is! Statistics courses in psychology today often cover structural equation modeling (SEM), a statistical tool that allows one to go beyond classical statistical models by combining them and adding more. Let’s explore what this means, what SEM really is, and SEM’s surprising parallels with the hippie culture! Continue reading

### Peter Edelsbrunner

Peter is currently doctoral student at the section for learning and instruction research of ETH Zurich in Switzerland. He graduated from Psychology at the University of Graz in Austria. Peter is interested in conceptual knowledge development and the application of flexible mixture models to developmental research. Since 2011 he has been active in the EFPSA European Summer School and related activities.

# Bayesian Statistics: Why and How

Bayesian statistics is what all the cool kids are talking about these days. Upon closer inspection, this does not come as a surprise. In contrast to classical statistics, Bayesian inference is principled, coherent, unbiased, and addresses an important question in science: in which of my hypothesis should I believe in, and how strongly, given the collected data?  Continue reading

### Fabian Dablander

Fabian Dablander is currently finishing his thesis in Cognitive Science at the University of Tübingen and Daimler Research & Development on validating driving simulations. He is interested in innovative ways of data collection, Bayesian statistics, open science, and effective altruism. You can find him on Twitter @fdabl.

# Of Elephants and Effect Sizes – Interview with Geoff Cumming

We all know these crucial moments while analysing our hard-earned data – the moment of truth – is there a star above the small p? Maybe even two? Can you write a nice and simple paper or do you have to bend your back to explain why people do not, surprisingly, behave the way you thought they would? It all depends on those little stars, below or above .05, significant or not, black or white. Continue reading

### Katharina Brecht

Aside from her role as Editor-in-Chief of the Journal of European Psychology Students, Katharina is currently pursuing her PhD at the University of Cambridge. Her research interests revolve around the evolution and development of social cognition.

# Bias in Conducting Research: Guidelines for Young Researchers Regarding Gender Differences

Every scientific discipline is determined by the object of measurement and the selection of appropriate methods of data collection and statistical analysis. Faulty methodology can lead to incorrect information in the results, without the researcher being aware of this. Taking incorrect knowledge as correct into account while conducting further research has far-reaching negative consequences. One of these errors present, to some degree, in every single research is bias. It is a particularly dangerous one, because it usually goes undetected by the researcher. But if you are aware of its threat there are ways to avoid it.  In research, it occurs when systematic error is introduced into sampling or testing by selecting or encouraging one outcome or answer over others. It comes in numerous ways and forms. The rest of this post will focus on causes of bias in the field of gender studies.

### Nina Jelić

Nina Jelić is a graduate psychology student at Faculty of Humanities and Social Sciences, University of Zagreb, Croatia. Her fields of interest are social and clinical psychology.

# Hunting For Significant Results: Don’t Do It

Many psychology students find themselves in a situation where their research did not yield any significant results. This can be immensely frustrating since they have put a lot of time and effort into designing the study, as well as in collecting and analyzing the data. In some cases, be it out of desperation or pressure to publish interesting findings, certain students will effectively “hunt” for results by conducting statistical tests on all possible variable combinations. For instance, after noticing that a hypothesized correlation between two variables proved to be non-significant, a student might create a correlation matrix of all continuous variables of her study and hope for at least one pair to be significantly related to each other. Other students might include one, two, or even more covariates in their analysis of variance (turning it into an ANCOVA), thereby hoping that the interaction they initially hypothesized between their key factors will become significant.

### Ezra Bottequin

Ezra Bottequin is a Master's student in affective and social psychology at the University of Geneva, Switzerland. He is the Editor-in-Chief of the Journal of European Psychology Students.

# Keep calm and be creative: Use mixed methods!

Having started my PhD in Psychology just recently, I have been a psychology student for a long time now. Doing a Bachelor’s and a Master’s degree has surely given me the chance to observe my own progression as a researcher as well as others. In my experience, a large number of students choose a very specific population of focus when it comes to their major projects. For example, a researcher might be interested to understand how international university students’ anxiety affects their concentration. Generally you might think that such a correlational research project would result in interesting findings – but what if it didn’t?

One of the best advice I have ever received from my lecturer is that the main purpose of major projects is not to publish significant results or to deliver a groundbreaking piece of research (although this is the ideal case scenario); it is to prepare us for the future and to make us good researchers when it counts (i.e. in the ‘real world’). While this is very realistic and somewhat reassuring, I firmly believe that there is one route that a lot of student researchers can take in order to ensure that they come out of the research process with rich, useful and satisfying data (because after all, we all have egos): by using mixed methods!   Continue reading

# How to spice up your presentations

There is nothing as dull in a student’s life as badly made PowerPoint presentations. Using PowerPoint has become a rule, whenever you present something in an university setting or otherwise. Everybody does it. And even when you follow all the hints on ‘how to make a good presentations’, like the ones Maris talked about in our last post at the JEPS Bulletin, you end up with just a PowerPoint presentation. How to change that and spice things up?

### Ivan Flis

Ivan Flis is a PhD student in History and Philosophy of Science at the Descartes Centre, Utrecht University; and has a degree in psychology from the University of Zagreb, Croatia. His research focuses on quantitative methodology in psychology, its history and application, and its relation to theory construction in psychological research. He had been an editor of JEPS for three years in the previous mandates.

# What makes a presentation good?

Presenting your research results might be the highlight in your undergraduate degree. This is your chance to tell the audience why your findings are relevant. What would make a good presentation? Naturally, the one that convinces them – your work has its place in the pool of knowledge. What’s the formula to make people listen (and follow your story)?

# When statistics doesn’t go as planned…

If you ever attended even the most basic statistics class, you have been warned about data manipulation. Even more so,if somebody mentions data manipulation and statistics, your mind inevitably leads you to the media. Reporting on scientific results derived from statistics, reporters often omit the warnings and precautions the authors themselves expressed on any far-reaching conclusions based on their results. But a cautious, scientifically sound conclusion does not cut it as a headline. Despite this being a serious issue, especially for psychologists, what better way to illustrate it than with a joke?

### Ivan Flis

Ivan Flis is a PhD student in History and Philosophy of Science at the Descartes Centre, Utrecht University; and has a degree in psychology from the University of Zagreb, Croatia. His research focuses on quantitative methodology in psychology, its history and application, and its relation to theory construction in psychological research. He had been an editor of JEPS for three years in the previous mandates.