Description Usage Format Details References Examples
Raymond (1987) describes data that were collected to investigate the usefulness of a newly developed instrument, the Foreign Language Attitude Scale (FLAS), for predicting success in the study of foreign languages. The dataset contains missing values.
1 |
A data frame with 279 observations on the following 12 variables.
lang
foreign language studied, a factor
with 4 levels ("french", "spanish", "german", "russian").
age
age group, a two-level factor
with
levels "-19"
= less than 20, and "20+"
= 20 and more.
priC
the number of prior foreign language courses; as
a 3-level factor w/ levels c("none", "1--2", "3+")
.
sex
M=male, F=female.
FLAS
score on foreign language attitude scale.
MLAT
Modern Language Aptitude Test, fourth subtest score.
vSAT
verbal score of Scholastic Attitude Test.
mSAT
math score of Scholastic Attitude Test.
eng
score on Penn State English placement exam.
HGPA
high scool grad point average.
CGPA
current college grad point average.
gradeB
final grade in foreign language course as
binary (hence the “B” in the variable name)
factor w/ levels "B-"
(B or lower), and "A"
.
This dataset is an adjusted version of the original dataset by Mark
Raymond. The adjustments can be looked up in Schafer (1997, p.200).
Where Schafer called the variables Y_1, Y_2, ...,
Y_{12}, then in the original miP package they had
all-capital colnames
, see FLAS.nms
in the example,
these have been slightly modified by Martin Maechler; also, the 5
factor
variables got meaningful levels
(instead of just integer codes 1
, 2
, ...).
Schafer, J.L. (1997) Analysis of Incomplete Multivariate Data Chapman and Hall, London.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | data(FLAS)
str(FLAS)
summary(FLAS) # summary *includes* number of NA's
## contingency table of three interesting factors -- including NA's
with(FLAS, table(sex, age, gradeB, useNA = "ifany"))
### The original (2011 `miP` CRAN package) data "labeling" and coding:
## 1) labeling all factors with integers; this is exactly
which(iF <- sapply(FLAS, is.factor)) # 1 2 3 4 12
FLAS.n <- FLAS
FLAS.n[iF] <- lapply(FLAS[iF], function(.) as.factor(as.integer(.)))
## 2) and using all capital variable names:
FLAS.nms <- c("LAN", "AGE", "PRI", "SEX", "FLAS", "MLAT",
"SATV", "SATM", "ENG", "HGPA", "CGPA", "GRD")
names(FLAS.n) <- FLAS.nms
## -> FLAS.n is what 'FLAS' used to be in 2011's miP version
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