A Weekend of Writing

Just a quick update post. I'm spending my weekend doing something I've wanted to do for years - I decided to join the International 3-Day Novel Contest. Every year, people around the world spend Labor Day weekend hunched over their computer or notebook, trying to write approximately 100 double-spaced pages (or more) of a complete novel. Writers submit their work, and in the Spring, the winner gets their book published by Anvil Press.

I'm stocked up on groceries, my dog is staying with a friend (who has also agreed to sign my witness affidavit, that I followed the rules of writing, most importantly that writing only occurred between Saturday from 12:00 am until Monday at 11:59 pm), and I've got 27 pages written. Let's do this.

Creating Things

 Normally, this time of year, we'd be getting excited for my choir's new season and rehearsals to begin in early September. Sadly, with the pandemic, it's unlikely we'll be getting together then, and I'm not sure how long it will take before it's safe and people begin feeling comfortable gathering in such a way. So I've been seeking out ways to keep some creativity in my life.

I've started drawing again, something I haven't done in years. I'm a bit rusty but hey - practice practice, right? I started with some pretty flowers from my parents' backyard, in a combination of soft chalk pastels (my favorite medium) and colored pencil:

And my next project is going to be a self-portrait, something I've never done before. Some early work with pencil that I'll fill in soon (thinking again a combo of colored pencil and chalk pastels):

I also had some fun putting together a Lego Architecture set of Paris:

What mainly sparked this round of creativity was writing and recording an arrangement for my choir's virtual benefit. I had so much fun with that, I'm going to keep doing it! I'm planning to share that video soon, and have also started recording some other a cappella arrangements I plan on sharing. 

And lastly, because I needed to bring Zep into the fun too, I've finally set up an Instagram for him. If you're on the 'gram, you can follow him here: https://www.instagram.com/zeppelinblackdog/

Talking with Numbers

From Reddit user Itsactuallywhom, a chart of US COVID-19 cases along with Trump's statements about the pandemic:

Illinois residents have been asked to shelter-in-place. I've got plans for writing projects, gardening, and an office to clean/reorganize, so I should be able to keep busy when I'm not working (from home). A few of my writing friends are talking about organizing CoroNaNo - National Novel Writing during COVID-19 quarantine. Anyone want to participate?

All Thumbs

Recently, I was diagnosed with flexor tendinitis in my left thumb. After weeks of pain, I finally saw an orthopedic specialist yesterday, who confirmed the original diagnosis, let me peek at my hand x-rays, and gave me my first (and hopefully last) cortisone injection. It left my thumb puffy, with a rather interesting almond-shaped bruise at the injection point.

If I thought the pain was bad before, it was nothing compared to the pain that showed up about an hour after injection. So severe, I could feel it in my teeth, and I spent the whole day feeling nauseous and jittery from it. Any movement of my thumb was excruciating. I stupidly ordered a dinner that required a knife and fork last night, but I discovered how to hold my fork between my forefinger and middle finger, with my pinky to steady it, much to the amusement of my dinner companion. I'm sure I looked like a kid just learning to use a fork. Who knows? Perhaps that will be a useful skill in the future.

I slept in my thumb brace with my hand resting flat on a pillow, and even still, woke up after every sleep cycle with my hand curled up and on fire.

Today, my thumb is stiff and sore, but I can almost make a fist before it starts to hurt, and I can grasp objects with it for short periods of time. The pain is minimal. It kind of makes yesterday's suffering worth it.

Best case scenario is that this injection cures my tendinitis. Worst case is that it does nothing, but based on how much better I'm feeling, I'm hopeful that isn't what's happening. Somewhere between best and worst case is that I may need another injection in the future. If I keep making a recovery, and feeling better tomorrow, I would probably be willing to do it again. But next time, I'll know not to try going to work after. I was useless and barely able to type.

I'm moving out of my apartment this weekend and I have a draft of a chapter I'm writing due Monday, so sadly, there will probably be no Statistics Sunday post this week. Back to regular posting in October.

Don't Upset a Writer

There's an old joke among writers: don't piss us off. You'll probably end up as an unflattering character or a murder victim in our next project.

There's another joke among writers: if you ever need to bump someone off or dispose of a body, ask a writer. Chances are he or she has thought through a hundred different ways to do it.

The thing about these jokes, though, is that writers usually get our retribution through writing. We don't tend to do the things we write about in real life. If someone legitimately encouraged us to cause real harm to another person or actually help in the commission of a crime, the answer would likely be a resounding "hell no." That's not how we deal with life's slings and arrows. Writing about them is generally enough to satisfy the desire and alleviate the pain.

But not in all cases.

If there were any rules about committing a crime (and I'm sure there are), rule #1 should be: don't write about it on the internet before you do it. And yet, a romance novelist may have done just that. Nancy Crampton Brophy has been charged with murdering her husband; this same author also wrote a blog post called "How to Murder Your Husband":

Crampton Brophy, 68, was arrested Sept. 5 on charges of murdering her husband with a gun and unlawful use of a weapon in the death of her husband, Daniel Brophy, according to the Portland Police Bureau.

The killing puzzled police and those close to Daniel Brophy from the start. Brophy, a 63-year-old chef, was fatally shot at his workplace at the Oregon Culinary Institute on the morning of June 2. Students were just beginning to file into the building for class when they found him bleeding in the kitchen, KATU2 news reported. Police had no description of the suspect.

In Crampton Brophy’s “How to Murder Your Husband” essay, she had expressed that although she frequently thought about murder, she didn’t see herself following through with something so brutal. She wrote she would not want to “worry about blood and brains splattered on my walls,” or “remembering lies.”

“I find it easier to wish people dead than to actually kill them,” she wrote. “. . . But the thing I know about murder is that every one of us have it in him/her when pushed far enough.”

It's kind of surprising to me that Brophy's books are romances and yet apparently involve a lot of murder and death. I don't read romance, so maybe murder and death is a common theme and I just don't know it. Of course, as I was thinking about last year's NaNoWriMo book, I surprised myself when I realized that the genre may, in fact, be romance. One without any murder, though.

This year's book will be an adventure/superhero story. And I'm excited to say I'm getting a very clear picture in my head of the story and its key scenes, an important milestone for me if I want to write something I consider good. I didn't have that for last year's project, which is why no one has seen it and I haven't touched it since November 30th. It was by design that I went into it without much prep - that's encouraged for NaNo, to write without bringing out the inner editor. And I'll admit, there are some sections in the book that are beautifully written. I surprised myself, in a good way, with some of it. But all-in-all, it's a mess in need of a LOT of work.

Statistics Sunday: What is Standard Setting?

In a past post, I talked about content validation studies, a big part of my job. Today, I'm going to give a quick overview of standard setting, another big part of my job, and an important step in many testing applications.

In any kind of ability testing application, items will be written with identified correct and incorrect answers. This means you can generate overall scores for your examinees, whether the raw score is simply the number of correct answers or generated with some kind of item response theory/Rasch model. But what isn't necessarily obvious is how to use those scores to categorize candidates and, in credentialing and similar applications, who should pass and who should fail.

This is the purpose of standard setting: to identify cut scores for different categories, such as pass/fail, basic/proficient/advanced, and so on.

There are many different methods for conducting standard setting. Overall, approaches can be thought of as item-based or holistic/test-based.

For item-based methods, standard setting committee members go through each item and categorize it in some way (the precise way depends on which method is being used). For instance, they may categorize it as basic, proficient, or advanced, or they may generate the likelihood that a minimally qualified candidate (i.e., the person who should pass) would get it right.

For holistic/test-based methods, committee members make decisions about cut scores within the context of the whole test. Holistic/test-based methods still require review of the entire exam, but don't require individual judgments about each item. For instance, committee members may have a booklet containing all items in order of difficulty (based on pretest data), and place a bookmark at the item that reflects the transition from proficient to advanced or from fail to pass.

The importance of standard setting comes down to defensibility. In licensure, for instance, failing a test may mean being unable to work in one's field at all. For this reason, definitions of who should pass and who should fail (in terms of knowledge, skills, and abilities) should be very strong and clearly tied to exam scores. And licensure and credentialing organizations are frequently required to prove, in a court of law, that their standards are fair, rigorously derived, and meaningful.

For my friends and readers in academic settings, this step may seem unnecessary. After all, you can easily categorize students into A, B, C, D, and F with the percentage of items correct. But this is simply a standard (that is, the cut score for pass/fail is 60%), set at some point in the past, and applied through academia.

I'm currently working on a chapter on standard setting with my boss and a coworker. And for anyone wanting to learn more about standard setting, two great books are Cizek and Bunch's Standard Setting and Zieky, Perie, and Livingston's Cut Scores.

Lots of Writing, Just Not On the Blog

Hi all! Again, it's been a while since I've blogged something. I'm currently keeping busy with multiple writing projects, and I'm hoping to spin one of them into a blog post soon:

• I'm still analyzing a huge survey dataset for my job, and writing up new analyses requested by our boards and advisory councils, as well as creating a version for laypeople (i.e., general public as opposed to people with a stats/research background)
• My team submitted to write a chapter for the 3rd edition of The Institute for Credentialing Excellence Handbook, an edited volume about important topics in credentialing/high-stakes testing; I'm leading our chapter on standard setting, which cover methods for standard setting (selecting exam pass points) and the logistics of conducting a standard-setting study
• I've already begun research for my NaNoWriMo novel, which will be a superhero story, so elements of sci-fi and fantasy and lots of fun world-building
• Lastly, I'm developing two new surveys, one for a content validation study and the other a regular survey we send out to assess the dental assisting workforce

Statistics Sunday: Using Text Analysis to Become a Better Writer

We all have words we love to use, and that we perhaps use too much. As an example: I have a tendency to use the same transitional statements, to the point that, before I submit a manuscript, I do a find all to see how many times I've used some of my favorites, e.g., additionally, though, and so on.

I'm sure we all have our own words we use way too often.

Text analysis can also be used to discover patterns in writing, and for a writer, may be helpful in discovering when we depend too much on certain words and phrases. For today's demonstration, I read in my (still in-progress) novel - a murder mystery called Killing Mr. Johnson - and did the same type of text analysis I've been demonstrating in recent posts.

To make things easier, I copied the document into a text file, and used the read_lines and tibble functions to prepare data for my analysis.

setwd("~/Dropbox/Writing/Killing Mr. Johnson")

library(tidyverse)

KMJ_text <- read_lines('KMJ_full.txt')

KMJ <- tibble(KMJ_text) %>%
  mutate(linenumber = row_number())

I kept my line numbers, which I could use in some future analysis. For now, I'm going to tokenize my data, drop stop words, and examine my most frequently used words.

library(tidytext)
KMJ_words <- KMJ %>%
  unnest_tokens(word, KMJ_text) %>%
  anti_join(stop_words)

## Joining, by = "word"

KMJ_words %>%
  count(word, sort = TRUE) %>%
  filter(n > 75) %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(word, n)) +
  geom_col() + xlab(NULL) + coord_flip()

Fortunately, my top 5 words are the names of the 5 main characters, with the star character at number 1: Emily is named almost 600 times in the book. It's a murder mystery, so I'm not too surprised that words like "body" and "death" are also common. But I know that, in my fiction writing, I often depend on a word type that draws a lot of disdain from authors I admire: adverbs. Not all adverbs, mind you, but specifically (pun intended) the "-ly adverbs."

ly_words <- KMJ_words %>%
  filter(str_detect(word, ".ly")) %>%
  count(word, sort = TRUE)

head(ly_words)

## # A tibble: 6 x 2
##   word         n
##   <chr>    <int>
## 1 emily      599
## 2 finally     80
## 3 quickly     60
## 4 emily’s     53
## 5 suddenly    39
## 6 quietly     38

Since my main character is named Emily, she was accidentally picked up by my string detect function. A few other top words also pop up in the list that aren't actually -ly adverbs. I'll filter those out then take a look at what I have left.

filter_out <- c("emily", "emily's", "emily’s","family", "reply", "holy")

ly_words <- ly_words %>%
  filter(!word %in% filter_out)

ly_words %>%
  filter(n > 10) %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(word, n)) +
  geom_col() + xlab(NULL) + coord_flip()

I use "finally", "quickly", and "suddenly" far too often. "Quietly" is also up there. I think the reason so many writers hate on adverbs is because it can encourage lazy writing. You might write that someone said something quietly or softly, but is there a better word? Did they whisper? Mutter? Murmur? Hiss? Did someone "move quickly" or did they do something else - run, sprint, dash?

At the same time, sometimes adverbs are necessary. I mean, can I think of a complete sentence that only includes an adverb? Definitely. Still, it might become tedious if I keep depending on the same words multiple times, and when a fiction book (or really any kind of writing) is tedious, we often give up. These results give me some things to think about as I edit.

Still have some big plans on the horizon, including some new statistics videos, a redesigned blog, and more surprises later! Thanks for reading!

Diagramming Sentences

Confession: I never learned to diagram sentences.

Second confession: I'm really upset about this fact.

Solution: This great article taught me how to do it!

Last confession: This GIF came up when I searched for "Happy nerd"

Statistics Sunday: Highlighting a Subset of Data in ggplot2

Here's my belated Statistics Sunday post, using a cool technique I just learned about: gghighlight. This R package works with ggplot2 to highlight a subset of data. To demonstrate, I'll use a dataset I analyzed for a previous post about my 2017 reading habits. [Side note: My reading goal for this year is 60 books, and I'm already at 43! I may have to increase my goal at some point.]

setwd("~/R")
library(tidyverse)

books<-read_csv("2017_books.csv", col_names = TRUE)

## Warning: Duplicated column names deduplicated: 'Author' => 'Author_1' [13]

## Parsed with column specification:
## cols(
##   .default = col_integer(),
##   Title = col_character(),
##   Author = col_character(),
##   G_Rating = col_double(),
##   Started = col_character(),
##   Finished = col_character()
## )

## See spec(...) for full column specifications.

One analysis I conducted with this dataset was to look at the correlation between book length (number of pages) and read time (number of days it took to read the book). We can also generate a scatterplot to visualize this relationship.

cor.test(books$Pages, books$Read_Time)

## 
## 	Pearson's product-moment correlation
## 
## data:  books$Pages and books$Read_Time
## t = 3.1396, df = 51, p-value = 0.002812
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1482981 0.6067498
## sample estimates:
##       cor 
## 0.4024597

scatter <- ggplot(books, aes(Pages, Read_Time)) +
  geom_point(size = 3) +
  theme_classic() +
  labs(title = "Relationship Between Reading Time and Page Length") +
  ylab("Read Time (in days)") +
  xlab("Number of Pages") +
  theme(legend.position="none",plot.title=element_text(hjust=0.5))

There's a significant positive correlation here, meaning the longer books take more days to read. It's a moderate correlation, and there are certainly other variables that may explain why a book took longer to read. For instance, nonfiction books may take longer. Books read in October or November (while I was gearing up for and participating in NaNoWriMo, respectively) may also take longer, since I had less spare time to read. I can conduct regressions and other analyses to examine which variables impact read time, but one of the most important parts of sharing results is creating good data visualizations. How can I show the impact these other variables have on read time in an understandable and visually appealing way?

gghighlight will let me draw attention to different parts of the plot. For example, I can ask gghighlight to draw attention to books that took longer than a certain amount of time to read, and I can even ask it to label those books.

library(gghighlight)

scatter + gghighlight(Read_Time > 14) +
  geom_label(aes(label = Title),
             hjust = 1,
             vjust = 1,
             fill = "blue",
             color = "white",
             alpha = 0.5)