here() starts at /home/markl/OneDrive/Teaching/20231-PSYC520/20231-psyc520-usc
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionverbaggr_dat <- read.table(
here("data", "data verbal aggression matrix dichot.txt"),
header = TRUE
)
# Rename due to a typo in original data
names(verbaggr_dat)[4:6] <- c("s2wantcurse", "s2wantscold", "s2wantshout")
names(verbaggr_dat)[16:18] <- c("s2docurse", "s2doscold", "s2doshout")
# Use small letters for all names
names(verbaggr_dat) <- tolower(names(verbaggr_dat))
# S1: A bus fails to stop for me
# S2: I miss a train because a clerk gave me faulty information.
# S3: The grocery store closes just as I am about to enter.
# S4: The operator disconnects me when I had used up my last 10 cents for a call.s1wantcurse s2wantcurse s3wantcurse s4wantcurse
0.7120253 0.7879747 0.5949367 0.6898734 # So, S2 > S1 > S4 > S3
# Coefficient of reproducibility
# Number of correct responses:
sum(
(verbaggr_dat$s2wantcurse >= verbaggr_dat$s1wantcurse) &
(verbaggr_dat$s1wantcurse >= verbaggr_dat$s4wantcurse) &
(verbaggr_dat$s4wantcurse >= verbaggr_dat$s3wantcurse)
)[1] 222[1] 94[1] 0.7025316What are the response labels for the bfi data set? Use ?bfi to identify them.
Answer:
Consider the wordings in the Extraversion items. Which items seem negatively oriented?
Answer:
What is the process of you reacting to
How positive or negative do you feel about nuclear fusion technology
on a 7-point scale (1 = extremely negative to 7 = extremely positive)?
Answer:
---
title: "Scaling"
format: html
---
```{r}
# install.packages("here")
library(here)
library(tidyr)
library(dplyr)
```
```{r}
verbaggr_dat <- read.table(
here("data", "data verbal aggression matrix dichot.txt"),
header = TRUE
)
# Rename due to a typo in original data
names(verbaggr_dat)[4:6] <- c("s2wantcurse", "s2wantscold", "s2wantshout")
names(verbaggr_dat)[16:18] <- c("s2docurse", "s2doscold", "s2doshout")
# Use small letters for all names
names(verbaggr_dat) <- tolower(names(verbaggr_dat))
# S1: A bus fails to stop for me
# S2: I miss a train because a clerk gave me faulty information.
# S3: The grocery store closes just as I am about to enter.
# S4: The operator disconnects me when I had used up my last 10 cents for a call.
```
## Guttman Scaling
```{r}
# Proportion of Yeses for want cursing
colMeans(verbaggr_dat[c(1, 4, 7, 10)])
# So, S2 > S1 > S4 > S3
# Coefficient of reproducibility
# Number of correct responses:
sum(
(verbaggr_dat$s2wantcurse >= verbaggr_dat$s1wantcurse) &
(verbaggr_dat$s1wantcurse >= verbaggr_dat$s4wantcurse) &
(verbaggr_dat$s4wantcurse >= verbaggr_dat$s3wantcurse)
)
# Number of errors
316 - 222
# C = 1 - number of errors / total number of responses;
# error in your text on p. 94
1 - 94 / 316
```
::: {.callout-note}
## Exercise 1
Compute the coefficient of reproducibility for the six S2 items
```{r}
# Add your code below
```
:::
## Likert Scaling
```{r}
data(bfi, package = "psych")
```
::: {.callout-note}
## Exercise 2
What are the response labels for the `bfi` data set? Use `?bfi` to identify them.
Answer:
:::
::: {.callout-note}
## Exercise 3
Consider the wordings in the Extraversion items. Which items seem negatively oriented?
Answer:
:::
::: {.callout-note}
## Exercise 4
Given that there are negatively oriented items, identify respondents who seem to have inconsistent responses in the Extraversion items. You can be creative in defining what inconsistent responses mean here, but please define them clearly.
```{r}
# Your code
```
:::
::: {.callout-note}
## Exercise 5
What is the process of you reacting to
> How positive or negative do you feel about nuclear fusion technology
on a 7-point scale (1 = *extremely negative* to 7 = *extremely positive*)?
Answer:
:::