Presentation at the 14th Annual Symposium on Second Language Research in Auckland, New Zealand Abstract: This presentation reports on a study that examined the contribution of word frequency to lexical diversity and sophistication in academic writing proficiency. Findings suggest that mid-frequency vocabulary may have a greater role than is typically given in the L2 composition classroom. Implications for practice and further analysis are discussed.

1. The Lexical Profile of
Diverse and
Sophisticated
Academic Essays
Melanie C.
Gonzalez, Ph.D.
Symposium on Second
LanguageWriting
Auckland University of
Technology
20 November 2015

2. The
intersection of
writing and
vocabulary
 Writing draws heavily on authors’ lexical faculties
 Even advanced multilingual (ML) writers exhibit large differences
in word knowledge from their monolingual English-speaking
(MES) peers
 Writing proficiency rubrics often feature a distinct category that
measures effective vocabulary deployment
Is it a question of teaching more words? If so, which words
do we teach?What lexical features should we focus on?
SSLW 2015 - M.Gonzalez, Ph.D, mgonzalez@salemstate.edu

3. The
intersection of
writing and
vocabulary
Rubric Lexical Criteria
ESL Composition Rubric
(Jacobs et al., 1981)
Sophisticated range; effective word choice;
word form mastery; appropriate register
TOEFL Independent
Writing
Variety and range of vocabulary, occasional
noticeable minor errors in word form and use
of idiomatic language; Appropriate word
choice and idiomaticity, minor lexical errors
IELTSTasks 1 and 2 Uses a wide range of vocabulary with very
natural and sophisticated control of lexical
features; rare minor errors occur only as
‘slips’; use of uncommon lexical items
Salem State’s ENL 110 FYC
Writing Rubric
Appropriate, effective, and precise word
choice for register and style
SSLW 2015 - M.Gonzalez, Ph.D, mgonzalez@salemstate.edu

4. Precedent
research
 Lexical diversity tends to be a strong indicator of writing
proficiency and sophisticated word use (Crossley & McNamara,
2009; Friginal, Li, &Weigle, 2014; Gonzalez, 2014; Johnson,
Acevedo, & Mercado, 2013;Yu, 2009)
 Lexical diversity - varied use of different words in writing
(Laufer & Nation, 1995)
 Interest in reexamining lexical frequency bands/word lists
(Gardner & Davies, 2013; Schmitt & Schmitt, 2012)
 Lexical frequency – “counts” of how often a word occurs in
language (Cobb, n.d.)
 Computational tools allow for current resurgence in profiling
lexical aspects of texts (see Cobb, n.d.; Gardner & Davies, 2014;
Graesser, McNamara, Louwerse, & Cai, 2004)
SSLW 2015 - M.Gonzalez, Ph.D, mgonzalez@salemstate.edu

5. Present study:
A pilot
How can we help our advanced ML writers to achieve lexical diversity
in their texts? Is it a question of teaching more words? If so, which
words do we teach?
RESEARCH QUESTION 1:
 How do the lexical frequency profiles of advanced ML writers’
academic compositions compare to those of their MES peers?
RESEARCH QUESTION 2:
 What frequency level(s) is a significant contributor to lexical
diversity in academic compositions?
SSLW 2015 - M.Gonzalez, Ph.D, mgonzalez@salemstate.edu

6. Methods
 172 essays (rated for English writing fluency by three independent
raters) Yielded 43 viable essays for analysis: 20 MES and 23 ML
writing samples
 Word count limit of 500 words (within 8 words +/- target)
 Analytical essays composed for grade; Earned a score of 5
using theTOEFL rubric
 ML writers’ L1s:Arabic (n=14), Spanish (n=3), Mandarin (n=5),
Korean (n=1)
 Used Schmitt and Schmitt’s (2012) lexical frequency categories:
 1K-3K = high-frequency terms
 4K-8K = mid-frequency terms
 9K+ = low-frequency terms
 Lexical frequency: BNC-COCA 25 (in Lextutor; Cobb, n.d.)
 Lexical diversity: MTLD (in Coh Metrix; Graesser et al., 2004)
SSLW 2015 - M.Gonzalez, Ph.D, mgonzalez@salemstate.edu

7. Table 1:
Descriptives
MES
(n=20)
ML (n=23) M SD Min Max
High-freq. MES 468.55 8.26 452.00 483.00
ML 487.17 8.29 452.00 502.00
Mid-freq. MES 14.45 6.01 3 24
ML 7.09 6.34 0 22
Low-freq. MES 3.20 2.14 0 7
ML 2.40 3.76 0 18
MTLD MES 79.95 17.35 46.85 120.86
ML 69.54 12.90 44.74 99.70
SSLW 2015 - M.Gonzalez, Ph.D, mgonzalez@salemstate.edu

8. Research
question 1
ANOVAANALYSIS
 ML writers used more high-frequency terms (F1,41=54.13, p<.00)
than MES writers
 MES writers used more mid-frequency words (F1,41=15.12, p<.00)
than ML writers.
 No statistical difference found in terms of either groups’ use of
low-frequency terms
 MES writers texts exhibited greater lexical diversity (F1,41=5.06,
p<.05) than their ML peers
SSLW 2015 - M.Gonzalez, Ph.D, mgonzalez@salemstate.edu

9. Research
question 2
MULTIPLE REGRESSION ANALYSIS
 The total variance explained by the model as a whole was 26.8%
(F3,39= 4.75, p < .05)
 Mid-frequency vocabulary was the only significant predictor of
lexical diversity
 As lexical diversity increases, there also an increase in the use of
mid-frequency terms (beta = .93, p < .05)
SSLW 2015 - M.Gonzalez, Ph.D, mgonzalez@salemstate.edu

10. Table 2:
Examples of
two essays
from the pilot
sample
1
1
4
5
5
32
447
1
3
4
9
2
28
43
396
K-8
K-7
K-6
K-5
K-4
K-3
K-2
K-1
Comparison of frequency profiles
MES ML
SSLW 2015 - M.Gonzalez, Ph.D, mgonzalez@salemstate.edu

11. Examples of
two essays
from the pilot
sample
One effect of eating fast food many
times during the day is gaining weight.
This is because these meals are
saturated with far, cholesterol, and
carbohydrates. So, people gain weight
more easily than others who eat
healthy food.This means that eating
fast food for long time will lead to
obesity.
As a result of gaining weight, people
cannot exercise. In fact, overweight
people think that exercise activities are
difficult for them because they cannot
even walk and move easily. So, they
prefer to stay away from fitness
centers, Therefore, those people gain
more and more weight.
Even worse, all the previous factors
lead to health problems. Due to eating
many fatty foods, people are likely to
suffer from high levels of cholesterol.
This causes heart diseases, such as
heart attack and other health
problems, like diabetes.
After having this experience, many gain a
new perspective on Christians. I have
learned that a lot people view Christians
as flawless people like in the Ned
Flanders example stated earlier.
However, most Christians considered
themselves indeed as flawed. When we
held a small group discussion, we shared
people’s stereotypical views about
Christians. Many of the students did not
like this sense that they were considered
“perfect” by outsiders because they
believe they came to god for help and to
repent their sins. In the group, we used
words such as legalistic, hypercritical,
boring, bible-pusher, and perfect to
describe the stereotypical Christian
according to others.When observing this
small group, I noticed that many of the
members wanted people to understand
them.The members were very warm and
welcoming. After attending three
meetings and two games they already
accepted me into their discourse
community
1 2
SSLW 2015 - M.Gonzalez, Ph.D, mgonzalez@salemstate.edu

12. Examples of
two essays
from the pilot
sample
One effect of eating fast food many
times during the day is gaining weight.
This is because these meals are
saturated with far, cholesterol, and
carbohydrates. So, people gain weight
more easily than others who eat
healthy food.This means that eating
fast food for long time will lead to
obesity.
As a result of gaining weight, people
cannot exercise. In fact, overweight
people think that exercise activities are
difficult for them because they cannot
even walk and move easily. So, they
prefer to stay away from fitness
centers, Therefore, those people gain
more and more weight.
Even worse, all the previous factors
lead to health problems. Due to eating
many fatty foods, people are likely to
suffer from high levels of cholesterol.
This causes heart diseases, such as
heart attack and other health
problems, like diabetes.
After having this experience, many gain a
new perspective on Christians. I have
learned that a lot people view Christians
as flawless people like in the Ned
Flanders example stated earlier.
However, most Christians considered
themselves indeed as flawed. When we
held a small group discussion, we shared
people’s stereotypical views about
Christians. Many of the students did not
like this sense that they were considered
“perfect” by outsiders because they
believe they came to god for help and to
repent their sins. In the group, we used
words such as legalistic, hypercritical,
boring, bible-pusher, and perfect to
describe the stereotypical Christian
according to others. When observing this
small group, I noticed that many of the
members wanted people to understand
them.The members were very warm and
welcoming. After attending three
meetings and two games they already
accepted me into their discourse
community.
1 2
SSLW 2015 - M.Gonzalez, Ph.D, mgonzalez@salemstate.edu

13. Discussion and
implications
 The finding that ML writers use more high-frequency terms than
their MES peers and that there is no difference between their use
of low-frequency items is in line with precedent research (see
Crossley & McNamara, 2009; 2012)
 MES writers employed double the number of mid-frequency
terms than ML writers
 Points to a possible gap in ML writers’ lexicons
 Guide students on how to use vocabulary profilers like
Lextutor
 During editing and peer-editing exercises, have students
identify overly repeated words and phrases
 Target mid-frequency vocabulary terms (precise synonyms)
and bundles that add variety to the text
SSLW 2015 - M.Gonzalez, Ph.D, mgonzalez@salemstate.edu

14. Limitations
 Pilot study (43* essays; 500 word count) – need more, longer
essays for examination
 Multiple regression does not like small sample sizes
 Longer essays may change the profile of the texts
 Genre was limited to analytical papers, but did not control for
topic
 Remove proper nouns from analysis
SSLW 2015 - M.Gonzalez, Ph.D, mgonzalez@salemstate.edu

15. Further
research
 Larger scale version of pilot to commence
 Examine the lexical frequency and diversity profiles of various
genres; same participants over time
 Address limitations
 Further studies in the research line:
 Include a measure of lexical recycling (Booth, 2014)
 Include a measure of lexical error
 Qualitative component - raters’ focus solely on lexical criteria
SSLW 2015 - M.Gonzalez, Ph.D, mgonzalez@salemstate.edu

16. Further
research
 If you currently teach advanced ML writers, please consider
contributing student essays to the larger study to run between
December 2015 and May 2016
 Please leave your email address with me and visit my Padlet
(http://padlet.com/ProfMelanie/lexicaldiversity) for more details
SSLW 2015 - M.Gonzalez, Ph.D, mgonzalez@salemstate.edu

17. References
Booth, P. (2014).The variance of lexical diversity profiles and its relationship to learning style. International
Review of Applied Linguistics in LanguageTeaching, 52(4), 357-375.
Cobb,T. (n.d.). Compleat LexicalTutor [Computer Program]. Retrieved from http://www.lextutor.ca
Crossley, S.A., & McNamara, D.S. (2009). Computational assessment of lexical differences in L1 and L2
writing. Journal of Second Language Writing, 18(2), 119-135.
Friginal, E., Li, M.,Weigle, S.C. (2014). Revisiting multiple profiles of learner compositions: A comparison of
highly rated NS and NNS essays. Journal of Second Language Writing, 23(1), 1-16.
Gardner, D., & Davies, M. (2014). A new academic vocabulary list. Applied Linguistics, 35(3), 305-327.
doi:10.1093/applin/amt015
Gonzalez, M.C. (2014, November). Lexical diversity, sophistication, and size in academic writing.
Presentation at the 13th Annual Symposium on Second LanguageWriting.
Graesser, A. C., McNamara, D. S., Louwerse, M. M.,&Cai, Z. (2004). Coh-Metrix: Analysis of text on cohesion
and language. Behavioral Research Methods, Instruments, andComputers, 36, 193–202.
Johnson, M., Acevedo, A., & Mercado, L. (2013). What vocabulary should we teach?: Lexical
frequency profiles and lexical diversity in second language writing. Writing & Pedagogy, 5(1), 82-103. doi:
10.1558/wap.v4i5.1
Laufer, B., & Nation, P. (1995). Vocabulary size and use: Lexical richness in L2 written production. Applied
Linguistics, 16(3), 307-322.
Schmitt, N., & Schmitt, D. (2012). A reassessment of frequency and vocabulary size in L2 vocabulary
teaching. LanguageTeaching, 47(4), 484-503.
Yu, G. (2009). Lexical diversity in writing and speaking task performances. Applied Linguistics, 31(2), 236-
259.
SSLW 2015 - M.Gonzalez, Ph.D, mgonzalez@salemstate.edu

18. Thank you!
Melanie Gonzalez mgonzalez@salemstate.edu
Please visit my Padlet to download a copy of this
presentation and others:
http://padlet.com/ProfMelanie/lexicaldiversity
This research and
presentation was funded in
part by Salem State
University's School of
Graduate Studies and the
Emilio and Mary DiFelice
Endowment for Research in
Education.