StatsTools
Heyo! Frederik, the author of papaja, requested that we update him with papers written with his package. I was like, oh man, like the whole lab?! So, I decided that I could probably make it easy by making a table here. Obviously, this table is current at the moment, as I hope many of the ones under review will get accepted, and I have several others that we will start writing soon. I only listed ones here you could find the actual .Rmd if you went to the links provided. Github is linked to each of these OSF pages as well.

OSF Title OSF Link Pre-Print Link Status
Methods to Detect Low Quality Data and Its Implication for Psychological Research https://osf.io/x6t8a/ https://osf.io/cv2bn/ 10.3758/s13428-018-1035-6
Does the Delivery Matter? Examining Randomization at the Item Level https://osf.io/gvx7s/ https://osf.io/p93df/ accepted pending small revisions
Beyond p-values: Utilizing Multiple Estimates to Evaluate Evidence https://osf.io/u9hf4/ https://osf.io/9hp7y/ revision to resubmit
Perceived Grading and Student Evaluation of Instruction https://osf.io/jdpfs/ https://osf.io/7x4uf/ revision to resubmit
Investigating the Interaction between Associative, Semantic, and Thematic Database Norms for Memory Judgments and Retrieval https://osf.io/y8h7v/ https://osf.io/fcesn/ under review
Bulletproof Bias? Considering the Type of Data in Common Proportion of Variance Effect Sizes https://osf.io/urd8q/ https://osf.io/cs4vy/ under review
The LAB: Linguistic Annotated Bibliography https://osf.io/9bcws/ https://osf.io/h3bwx/ under review
English Semantic Feature Production Norms: An Extended Database of 4,436 Concepts https://osf.io/cjyzw/ https://osf.io/gxbf4/ under review
A Meta-Analysis of Expressive Writing on Positive Psychology Variables and Traumatic Stress https://osf.io/4mjqt/ https://osf.io/u98cw/ under review
The N400’s 3 As: Association, Automaticity, Attenuation (and Some Semantics Too) https://osf.io/h5sd6/ https://osf.io/6w2se/ under review
Focus on the Target: The Role of Attentional Focus in Decisions about War https://osf.io/r8qp2/ https://osf.io/9fgu8 under review
An Extension of the QWERTY Effect: Not Just the Right Hand, Expertise and Typability Predict Valence Ratings of Words https://osf.io/zs2qj/ https://osf.io/k7dx5/ under review
Modeling Memory: Exploring the Relationship Between Word Overlap and Single Word Norms when Predicting Relatedness Judgments and Retrieval https://osf.io/j7qtc/ https://osf.io/qekad/ writing
Outrageous Observations: The Redheaded Stepchild of Data Analysis https://osf.io/52mqw/ writing
Moral Foundations of U.S. Political News Organizations https://osf.io/5kpj7/ writing
A Validation of the Moral Foundations Questionnaire and Dictionary https://osf.io/kt9yf/ writing

You can also check out the YouTube for the couple of videos I’ve made on papaja and markdown.

Heyo!

I have so much stuff backlogged to blog about – especially that we are working on fully integrating to OSF and putting up preprints of the cool work we are doing! But this blog post is reserved for HOW EXCITED I AM to announce that MOTE is ready to go to import into R. Run this code in your R:

install.packages(“devtools”) ##only needed if you do not have it yet

devtools::install_github(“doomlab/MOTE”)

Remember that “” sometimes does not copy correctly into R. Go nuts! Ask questions! Give feedback! One thing I did not talk about in the video is a limitation of V in chi-square. Due to the distribution of chi-square, V confidence intervals are only useful on smaller r x c combinations (like 2X2, 3×3). After you hit about 4 rows/columns, the distribution flattens out, and the calculated confidence interval is not around the V value.  For example, a X2 of 14 with sample size 100, with four rows and columns gives you:

v.chi.sq(x2 = 14, n = 100,r = 4, c = 4, a = .05)
$v
[1] 0.6480741

$vlow
[1] 0.1732051

$vhigh
[1] 0.3241347

$n
[1] 100

$df
[1] 9

$x2
[1] 14

$p
[1] 0.1223252

Warning message:
The size of the effect combined with the degrees of freedom is too small to determine a lower confidence limit for the ‘alpha.lower’ (or the (1/2)(1-‘conf.level’) symmetric) value specified (set to zero).

As you can see, this is a limitation of confidence intervals on chi-square. Also, I found more typos :|.

Go check out github:

https://github.com/doomlab/MOTE

Go check out the video on how to install and the history of MOTE: