Stats Doesn't Have to be Scary!

Looking for a free, open-source, easy-to-use statistics program? If you haven't heard about JASP before, then I suggest you read my blog more often! 😂 I've already promoted the use of JASP in the classroom and in research, despite a few limitations such as the inability to edit graphs.

Check out the full playlist: 

https://www.youtube.com/playlist?list=PLvPJKAgYsyoJxXZn-fTmqxF_GfOQ-htvJ

In trying to probe the limits of JASP, I uploaded a dataset with over 40,000 rows and 6 or 7 columns of data. It took a minute to upload such a large file, but there was no problem with running analyses even on a large dataset such as that one!

I think most behavioral researchers could spend their entire careers using this program, as long as they also have another program handy to generate publication-quality graphs. R is a popular solution for this (though you'll have to learn a bit of programming to use it).

Benjamin Nanes, MD, PhD, recommends additional options such as ImageJ or Inkscape. They're free, which is a big plus for impoverished graduate students and/or those who simply want to avoid the hassle of trying to get a license for SPSS or another such program on their personal computers.

Though I haven't tried these myself (yet!), I trust Dr. Nanes' recommendations and plan to try them out soon. Another option, also recommended by Dr. Nanes, is Inkscape: this could be used to add text (such as axis labels) to the graph generated by JASP and export it in a vector format that your journal will accept.

Everything I've said so far about JASP also goes for jamovi, another free and open-source program with a user-friendly interface. JASP started development before jamovi, so it's a little further along in its capabilities, but the original lead programmer for JASP is the lead programmer for jamovi, so there are many similarities between the programs--and I like both of them! jamovi does have a few features that JASP lacks, including the ability to see the R syntax for a given operation. This makes jamovi a great bridge for those who would like to learn R!

Since both JASP and jamovi are based on R but provide a far more visually appealing user interface, the analyses are trustworthy (though I've double-checked some analyses myself) and the programs themselves are easy to use.

In any case, if you're wondering why I like JASP so much, I made and edited a series of videos yesterday showing how to install JASP, upload files, and run most of the common tests in JASP 0.8.6.0. I've compiled these videos into a YouTube playlist; note that the instructions for jamovi are going to be quite similar.

screenshot of JASP 0.8.6.0 from my own computer

If you haven't already tried JASP or jamovi, what are you waiting for?

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Wondering about the social media usage of actual college students? 

Check out the results of this totally informal—but real—survey.

In case you missed it, I review some fantastic, easy-to-use, and FREE stats programs here.

For more help explaining statistical concepts and when to use them, 

please download my freely available PDF guide here!

Is Wikipedia in crisis?

A week ago, WIRED published an opinion piece lamenting the decline of the pursuit of knowledge, and claiming that Wikipedia is in crisis.

I saw the headline, "Social Media is Killing Wikipedia," in an e-mail from LinkedIn. The link led to a brief summary of the article (not the article itself...) along with an extensive discussion chain, complete with the hashtag #WikipediaFuture.

Cute.

I clicked it because I suspected the headline would prove to be an assertion that was either exaggerated or simply untrue. In essence, clickbait.

Alas, I wasn't surprised.

A replacement for SPSS?

Could this program be the end of SPSS?

I have previously recommended JASP as a useful—and free!—statistical software package. I stand by that recommendation (nay, I'm doubling down on it!) as JASP has the following advantages:

1. A slick, easy-to-grasp user interface
2. All of the major types of statistical test, including one-sample, repeated-measures, and independent-samples t tests, ANOVA, ANCOVA, correlation, regression, and even the chi-square test for independence [i.e. the two-variable chi-square]. It even has a module for structural equation modeling, for those who conduct such analyses!
3. Bayesian analogues to each of the above tests
4. A simple, one-click method to run these tests, which makes it an ideal instructional tool (and useful for many basic research needs as well). 
5. It's a no-cost, open-source, cross-platform (Windows, MacOS, Linux) program, so there are zero barriers to personal use.
6. It launches pretty quickly, and runs extremely fast—even on low-powered computers. 

JASP Screenshot from my own personal computer 

This image is freely available for use; just cite http://psychsci.blogspot.com/2017/04/a-replacement-for-spss.html

The recent [March 21, 2017] release of version 0.8.1.1 has rendered JASP is even more useful than it was in the past! Here's the latest major change to the program:

• Data synchronization that (finally!) allows you to edit your data from within the program itself. You can sync a .csv file, .sav file, or .ods [LibreOffice spreadsheet] file.
• By the way, LibreOffice is a great, free alternative to Microsoft Office. I encourage you to check it out and shed the shackles of expensive, closed-source software!

Now that you can edit the data in a window in the statistical program itself (via data synchronization, which can be turned off if you so desire), and since a previous build allowed users to integrate JASP output with their OSF page, I think that JASP has finally become good enough to provide many researchers with all the statistical capability they need!

SPSS can still perform some of the more esoteric/advanced statistical procedures that JASP cannot, such as multi-level modeling. But since such procedures tend to be used relatively infrequently (at least in experimental social science research such as my own), JASP can probably handle the bulk of your analytical load.

Further, as a stats instructor, this tool is my secret weapon! I am encouraging students to use this program for an APA-style paper for which they have to run a handful of analyses to answer different questions.

This semester, I asked my Stats students how they felt about SPSS, and they generally weren't too fond of the program due to its complexity, pickiness, and uninformative error messages (not to mention an appearance that's stuck in the 1990s).

After I demonstrated JASP in class, the students seemed far more impressed with the free and open JASP than they were with the costly SPSS!

EDIT 5/30/2018: Just discovered that JASP is now available as an online resource, according to this blog post on the JASP website and a Tweet by E.J. Wagenmakers. You have to sign up for a RollApp account if you want to use this option.

And, if for some reason you're not a fan of JASP, a similar (and also free!) option called jamovi is under development. You can type data straight into this one, whereas I don't believe that's an option yet in JASP. jamovi lacks some features that JASP incorporates, but it's still a nice stats program for use in the classroom (or for your research)!

jamovi screenshot from my own personal computer

This image is freely available for use; just cite http://psychsci.blogspot.com/2017/04/a-replacement-for-spss.html

Should IBM be worried about SPSS adoption rates? Maybe...

Intrigued? Here is the website; you can download JASP by clicking 

the "Download" tab and selecting the version that's appropriate for your operating system. 
 

Or you can just follow this link instead. Hey, what do you have to lose? 

Try out this free stats program and see if it meets your needs! If it doesn't, you can just uninstall it...

If you'd like to try jamovi, here's the link for that.

Stat-ception: Everything you think you know about psych stats is wrong!

In the spirit of open science, I have posted a video of a talk on statistical practice that I gave in the Cognition Forum at Bowling Green State University.


This talk was in 2 parts; the first part summarizes many of the common objections to null hypothesis significance testing (NHST) that thinkers have made over the decades, and the second part goes over my current recommendations to tackle the problem.

 

Part I is available at https://youtu.be/JgZZkMJhPvI; Part II is forthcoming! I've also embedded the video right here:

You can view and download the full slideshow at https://drive.google.com/open?id=0B4ZtXTwxIPrjTktiMGdoQ3JBSHM. The free (and very easy-to-use!) statistical program JASP can be found at https://jasp-stats.org/. JASP is useful if you want to run the analysis on the precision-vs-oomph example that I discuss at the end of the video (at the 39:41 mark).

I have already tackled some of the issues with NHST on more than one occasion in prior posts here, and I have also provided a practical guide to psych stats as a freely available educational resource!

There are a variety of excellent papers on the topic of statistical practice in social science fields; my working paper on the subject summarizes them. In the interest of open science, I've made this working paper available at https://osf.io/preprints/psyarxiv/hp53k/. Other great resources on the topic include Gigerenzer (2004) and Ziliak & McCloskey (2009), which are also freely available.

The Simple Life: Graphs to aid understanding of the results

In my first publication, my advisor and I created graphs according to the guidelines listed on the Judgment and Decision Making journal's webpage. My advisor and I both maintain a general preference to see results presented visually, in the form of easily-understood, properly-labeled graphs. However, in correspondence with JDM's editor, he felt that the results were simple enough to understand, and the graphs therefore weren't really necessary.

I think the editor's idea was that:
a) not including the graphs would save space, and
b) including the graphs would probably involve a bunch of difficult/time-consuming formatting. I certainly don't blame the editor for advising us to exclude the graphs; I understand perfectly where he's coming from!

So, to save time, space, and effort, our graphs were never published. Until now.

The graph for Experiment 1, which fits best with the information presented near the top of p. 305, presents the proportion of participants' decisions that were consistent with pre-exposure (bar on left) and with the recognition heuristic (as determined by participants' self-reported recognition; bar on right). Error bars represent 95% confidence intervals.



The graph for Experiment 2, below, illustrates the data presented on p. 307-308. The dots represent the mean proportion of choices that were consistent with the recognition heuristic, for participants in each of the three training conditions. Again, error bars indicate 95% confidence intervals, which I recommend reporting as part of a more complete picture of your data.

Here's a link to the full-size .jpg image for the first graph, and here's the link for the second graph.