Description
I have attached an article from this article thesis is a model and from this I have done the assessment all I need you is to paraphrase the assessment and make sur the words are 800
Unformatted Attachment Preview
Studies in Second Language Learning and Teaching
Department of English Studies, Faculty of Pedagogy and Fine Arts, Adam Mickiewicz University, Kalisz
SSLLT 12 (3). 2022. 435-458
http://dx.doi.org/10.14746/ssllt.2022.12.3.5
http://pressto.amu.edu.pl/index.php/ssllt
The role of motivation and vocabulary learning
strategies in L2 vocabulary knowledge:
A structural equation modeling analysis
Jang Ho Lee
Chung-Ang University, Republic of Korea, Seoul
https://orcid.org/0000-0003-2767-3881
[email protected]
Joung Joo Ahn
Cyber University of Korea, Republic of Korea, Seoul
https://orcid.org/0000-0001-8923-8098
[email protected]
Hansol Lee
Korea Military Academy, Republic of Korea, Seoul
https://orcid.org/0000-0002-6912-7128
[email protected]
Abstract
This study explores the complex relationships between language learning motivation, vocabulary learning strategies, and two components of second language vocabulary knowledge (i.e., vocabulary size and depth), within the
framework of self-regulated learning. Responses to questionnaires were gathered from 185 secondary-level Korean adolescent learners of English as a foreign language, regarding their motivation and vocabulary learning strategy
use; additionally, the results of their vocabulary size and depth tests were collected. We adopted structural equation modeling for analysis, with vocabulary
learning strategies consisting of memory, cognitive, and metacognitive categories, and vocabulary knowledge consisting of vocabulary size and depth. The results showed that motivation directly predicted vocabulary learning strategies
435
Jang Ho Lee, Joung Joo Ahn, Hansol Lee
and vocabulary knowledge, and indirectly predicted vocabulary knowledge
via vocabulary learning strategies. When further classified, intrinsic motivation was found to have a stronger influence on the use of vocabulary learning
strategies and vocabulary knowledge than extrinsic motivation. We discuss
the implications of increasing learners’ motivation and repertoire of strategies
for improving vocabulary size and depth.
Keywords: motivation; self-regulated learning; vocabulary depth; vocabulary
learning strategies; vocabulary size
1. Introduction
Vocabulary plays a significant role in second language (L2) learning and teaching
(Nation, 2013; Schmitt, 2008). Based on empirical evidence in this regard, researchers have focused on identifying ways to improve learners’ L2 vocabulary knowledge
by either providing various types of treatment or accommodation (Lee et al., 2019;
Laufer, 2009) or promoting influential learner factors such as motivation and learning strategies (e.g., Barcroft, 2009; Fontecha & Gallego, 2012; Gu & Johnson, 1996;
Zhang & Lu, 2015). The present study focuses on the latter aspect and explores the
complex relationships between L2 learners’ motivation, their use of vocabulary
learning strategies (VLS), and two components of vocabulary knowledge.
Previous studies that have examined the relationships between language
learning strategies (LLS) and different dimensions of L2 skills/knowledge suggest that
the former facilitate L2 learning (e.g., Bećirović et al., 2021; Cáceres-Lorenzo, 2015;
Macaro, 2001; Yu, 2019). Research on L2 vocabulary has further explored this relationship by investigating the strategies or combinations of strategies that successful
learners use to broaden L2 vocabulary (e.g., Kojic-Sabo & Lightbown, 1999; Zhang &
Lu, 2015). The role of motivation in L2 vocabulary learning has also gained researchers’
attention. With motivation being one of the most important factors in L2 learning
(Csizér, 2019; Dörnyei, 2020), a few studies have examined the relationship between
motivation and L2 vocabulary knowledge (e.g., Alamer, 2022; Fontecha & Gallego,
2012; Lee, 2017). These studies indicate a close relationship between the constructs.
However, despite the increasing interest, few attempts have been made
to explore the relationships between these two important learner factors and
vocabulary knowledge. In addition, previous studies have suffered from limitations. First, they tapped into either VLS (e.g., Kojic-Sabo & Lightbown, 1999;
Zhang & Lu, 2015) or motivation (e.g., Fontecha & Gallego, 2012; Zheng, 2012)
to explore vocabulary knowledge. However, it may be worthwhile to examine
these variables together in view of the proposition that motivated learners are
likely to employ more strategies to foster their L2 learning (Lou & Noels, 2019).
436
The role of motivation and vocabulary learning strategies in L2 vocabulary knowledge: A structural. . .
This proposition has gained empirical support in recent studies (Lee, 2020;
Zhang et al., 2017) in L2 vocabulary learning contexts. Second, most studies (e.g.,
Gu & Johnson, 1996; Lee, 2020; Zhang & Lu, 2015) have primarily recruited undergraduate L2 learners as participants who, arguably, may have already acquired
a large L2 vocabulary. Finally, most of the existing empirical investigations have only
examined one aspect of vocabulary knowledge, that is, vocabulary size (breadth)
(e.g., Kojic-Sabo & Lightbown, 1999; Zhang et al., 2017). However, in view of the
current thinking regarding operationalizing this target knowledge in the field, it
seems beneficial to consider both aspects of L2 vocabulary knowledge together
(see Schmitt, 2014 for an in-depth review of this issue). Additionally, certain VLS
(e.g., paying attention to diverse aspects of a target word) may be associated with
the development of depth knowledge; that is, adding the depth aspect of L2
vocabulary knowledge may facilitate better measurement of the relationship
between a range of VLS and L2 vocabulary knowledge.
In view of the aforementioned limitations, the present study aims to examine the structural relationships among different types of L2 motivation, VLS, and
two components of L2 vocabulary knowledge (i.e., size and depth) of adolescent
learners of English as a foreign language (EFL) using the structural equation modeling (SEM) analysis. This attempt draws on self-regulated learning (SRL) as the
theoretical framework, assuming that “self-regulated students activate, alter, and
sustain specific learning practices” (Zimmerman, 2002, p. 70), taking the initiative
and responsibility for their own learning. Within this framework, we hypothesize
that motivated L2 learners would likely employ more VLS, which would, in turn,
contribute to the expansion of their L2 vocabulary size and depth knowledge.
2. Literature review
In this section, we first review the literature on the relationships between motivation and L2 vocabulary knowledge, followed by the studies on the relationships between VLS and L2 vocabulary knowledge. Thereafter, we review the studies on the
relationships between two major predictors of this study (i.e., motivation and VLS)
and outcome variables (i.e., different components of L2 vocabulary knowledge).
2.1. Relationships between motivation and L2 vocabulary knowledge
The concept of language learning motivation has received considerable attention in the field of L2 learning (e.g., Boo et al., 2015; Dörnyei, 2009, 2020; Ushioda, 2019; Wu, 2003). Research in this area has drawn on different theoretical
frameworks, such as the socio-educational model (Gardner, 1985, 2010), the selfdetermination theory (SDT) (Ryan & Deci, 2000, 2017), and the L2 motivational self
437
Jang Ho Lee, Joung Joo Ahn, Hansol Lee
system (Dörnyei, 2005, 2009). We adopted the SDT as our theoretical framework
of L2 learning motivation because of its suitability to account for different stages
of L2 learning motivation (see next paragraph for details) among secondary-level
Korean EFL learners (Jang & Kim, 2014; Woo, 2007). Furthermore, in view of the
suggestion that the socio-educational model may be better oriented toward intercultural and community-related phenomena (than learning), and the L2 motivational self system may be suitable for older learner groups who may have
developed greater capability of visualizing an ideal self (Sugita McEown et al.,
2014), the SDT appears to be a better fit for our purpose.
The SDT differentiates between “types of motivation” along a continuum,
from “controlled to autonomous” (Ryan & Deci, 2017, p. 3). At the two opposite
ends of this continuum there lie amotivation (i.e., lack of motivation) and intrinsic motivation. Intrinsically motivated learners study L2 “because of the inherent
pleasure in doing so,” whereas amotivation arises “when a learner has no
goals … for learning a language” (Noels et al., 2001, p. 426). In the middle of the
continuum there lies extrinsic motivation, which can be subdivided into external
regulation (learning regulated by external rewards or punishments), introjected
regulation (learning controlled to some degree by internal feelings or pressure),
and identified regulation (learning resulting from a conscious valuing and acceptance of personal goals) (Ryan & Deci, 2000). Learners oriented toward external regulation and introjected regulation will “stop putting effort into L2
learning once the pressure is lifted” (Noels et al., 2001, p. 425), whereas those
oriented toward intrinsic motivation and identified regulation are relatively
more self-determined; hence, they are more persistent in their efforts to learn.
A previous finding based on Korean EFL contexts (Jang & Kim, 2014) confirmed
this assumption, revealing that intrinsic motivation was positively related to secondary-level students’ English proficiency levels.
The importance of motivation in language learning has been highlighted
in the literature (e.g., Csizér, 2019; Dörnyei, 2019, 2020; Ryan & Deci, 2017), and
L2 vocabulary acquisition research is no exception in this regard (e.g., Laufer &
Hulstijn, 2001; Papi, 2018; Zheng, 2012). An important contribution in this regard is Tseng and Schmitt’s (2008) study, in which they proposed a model of
vocabulary learning by taking a process-oriented approach, and operationalizing
vocabulary learning as a cyclical process. This model proposed that motivational
constructs influence the development of vocabulary knowledge, which is succinctly summarized by the authors as follows: “motivation appears to be involved in all stages of [vocabulary] learning (instigating, sustaining, and evaluating), thus permeating the whole process” (Tseng & Schmitt, 2008, p. 383).
However, extant studies on the relationships between motivation and L2
vocabulary knowledge have produced rather mixed findings (e.g., Alamer, 2022;
438
The role of motivation and vocabulary learning strategies in L2 vocabulary knowledge: A structural. . .
Canga Alonso & Fontecha, 2014; Fontecha & Gallego, 2012). For example, Fontecha and Gallego’s research (2012) measured receptive vocabulary knowledge
and motivation to learn English among secondary-level EFL Spanish students studying in the 8th and 9th grades. They found that the students with higher motivation scored higher on receptive vocabulary tests than those with lower motivation
in the 9th grade; however, the same pattern was not found for students in the 8th
grade. More recently, Alamer’s (2022) study with 366 Saudi EFL students, based
on the SDT framework, revealed that autonomous motivation (i.e., the construct
consisting of intrinsic motivation and identified regulation) positively predicted
vocabulary size, whereas controlled motivation (i.e., the construct consisting of
introjected regulation and external regulations) negatively did so. The review of
these studies not only underscores the need to measure the subconstructs of motivation, but also suggests that other variables could be at play, mediating the relationships between motivation and L2 vocabulary knowledge. In this regard, we
turn to one of such potential mediating variables: VLS.
2.2. Relationships between vocabulary learning strategies and L2 vocabulary
knowledge
Influenced by research on language learning strategies (e.g., O’Malley & Chamot,
1990; Oxford, 1990), a few L2 vocabulary studies have presented a domain-specific group of learning strategies (i.e., VLS), which refers to “a wide spectrum of
strategies used as part of an on-going process of vocabulary learning” (Gu &
Johnson, 1996, p. 669). Gu and Johnson (1996) distinguished between metacognitive regulation and cognitive strategies, with the former consisting of selective
attention and self-initiation, and the latter including guessing, dictionary, notetaking, rehearsal, encoding, and activation. Another key work in this area is
Schmitt’s (1997) inventory of VLS. Adopting certain major categories from Oxford’s (1990) classification, Schmitt first classified VLS into two broad groups:
discovery strategies and consolidation strategies. Discovery strategies aim to determine the meaning of new and unfamiliar words. These strategies are subdivided into determination strategies (e.g., guessing the meaning of a new word
from its form or contexts, or referring to resources, such as dictionaries) and
social strategies (i.e., asking others for the meaning of a new word). By contrast,
consolidation strategies are concerned with remembering introduced words,
and are subdivided into memory (i.e., learning vocabulary by executing manipulative mental processing), cognitive (e.g., repetition and using mechanical
means such as word lists and vocabulary notebooks), metacognitive (i.e., selfregulating one’s own vocabulary learning), and social strategies (e.g., learning
or practicing vocabulary with peers).
439
Jang Ho Lee, Joung Joo Ahn, Hansol Lee
Based on previous research on the classification of VLS, several studies
have examined the relationship between VLS and vocabulary knowledge using
different methodological approaches. One group of such empirical investigations
employed cluster analysis as a method of analyzing data, which enables the identification of distinctive clusters with different learner profiles. For example, in
the aforementioned study by Gu and Johnson (1996), 850 non-English-major
undergraduate Chinese EFL learners were asked to respond to a VLS questionnaire and take a vocabulary size test (VST). The results revealed that strategies
such as semantic encoding, word list learning, and contextual encoding, among
VLS, were significantly related to vocabulary size. Furthermore, the participants
could be grouped into five different clusters based on different patterns of VLS
use, with two of them constituting the majority and the only difference between
the two being the use of encoding strategies (i.e., encoders and non-encoders),
another two being high achievers (i.e., active strategy users and readers), and
the other being low achievers (i.e., passive strategy users). With a similar aim in
mind, Kojic-Sabo and Lightbown (1999) conducted a cluster analysis to examine
43 EFL and 47 English as a second language (ESL) students’ strategic approaches
to vocabulary learning and their relationships with vocabulary breadth. The participants’ VLS were grouped into five categories, namely “(a) time, (b) learner
independence, (c) vocabulary notes, (d) review, and (e) dictionary use” (p. 179).
Among the eight clusters generated by the cluster analysis, it was found that
those clusters with little use of VLS had relatively low vocabulary breadth. In
contrast, the two clusters which reported greater use of VLS had the largest vocabulary breadth, but these two clusters were rather different, in that one selectively used only certain types of VLS, and the other used all types of VLS to
more or less the same degree. Based on this result, Kojic-Sabo and Lightbown
(1999) suggest that “specific combinations of some of the strategies are as effective as the use of all five [strategies]” (p. 189).
Some of the more recent studies have employed the SEM to investigate
the relationships between VLS and vocabulary knowledge. It should be noted
here that they began to see vocabulary knowledge operationalized as constituting different components, including vocabulary size and depth knowledge, with
the former and the latter referring to “how many words are known” in terms of the
form-meaning link and “how well those words are known” in terms of diverse aspects of vocabulary (e.g., collocations, multiple senses), respectively (Schmitt, 2014,
p. 914). As one of such studies, Zhang and Lu (2015) administered a battery of vocabulary tests as well as a questionnaire on VLS to 150 Chinese EFL undergraduate students. The VLS were categorized into five factors: form (i.e., mnemonic
strategies based on studying the form of vocabulary), association (i.e., mnemonic
strategies based on associating words with semantically or morphologically related
440
The role of motivation and vocabulary learning strategies in L2 vocabulary knowledge: A structural. . .
ones), repetition (i.e., cognitive strategies based on repetition), word list (i.e.,
cognitive strategies based on word lists), and picture/image (i.e., mnemonic
strategies based on associating the vocabulary with images or situations). The
results revealed that both form and association positively predicted vocabulary
size and depth, whereas word list had a negative effect. Similarly, Fan (2020)
employed both vocabulary size and depth tests, as well as the VLS questionnaire,
using the SEM approach, with 419 Chinese EFL undergraduate students. The results of this study showed that attention (i.e., attending to vocabulary during
reading English texts or watching English media) and guessing (i.e., guessing the
meaning of words from the textual or situational contexts) positively predicted
both types of vocabulary knowledge, whereas socializing (i.e., asking others
about the meaning and use of vocabulary) had a negative effect.
Although the studies mentioned above identified a close relationship between VLS and L2 vocabulary knowledge, researchers (e.g., Gu & Johnson, 1996;
Kojic-Sabo & Lightbown, 1999) suggest that future studies should further examine the role of motivation, which may play an important part in such a relationship. In the following section, we review studies that have examined the complex relationships between motivation, VLS, and L2 vocabulary knowledge.
2.3. Relationships between motivation, vocabulary learning strategies, and L2
vocabulary knowledge
In the field of L2 research, a few studies have revealed a close relationship
among motivation, language learning strategy use, and achievement operationalized as general proficiency level or knowledge in specific domains (e.g., Matsumoto et al., 2013; Yamamori et al., 2003). In L2 vocabulary research, Zhang et al.
(2017) were among the first to explore the relationships between motivation,
strategy use, and L2 vocabulary knowledge in a single study. Within the SRL
framework, their study, including 107 10th grade Chinese EFL learners and using
the SEM approach, revealed that VLS mediated the association between motivation and vocabulary size. However, when motivation was specified as either
intrinsic motivation or extrinsic motivation, extrinsic motivation did not directly
predict vocabulary size (but did so indirectly via VLS). Intrinsic motivation directly and indirectly predicted vocabulary knowledge and had a greater influence on the use of VLS. The authors concluded that “[l]earners need to have
autonomous intrinsic motivation to use various learning strategies” and highlighted that intrinsically motivated learners “actively seek out useful resources
that could help with their learning” (Zhang et al., 2017, p. 69).
In another study, this time involving 492 Korean undergraduate students
registered in an English academic writing class, Lee (2020) tested the structural
441
Jang Ho Lee, Joung Joo Ahn, Hansol Lee
model of L2 aptitude, motivation, language processing experience, and two components of L2 vocabulary knowledge (i.e., size and depth). The results of the structural model revealed that motivation directly predicted strategy use, and indirectly predicted both components of vocabulary knowledge via the mediation of
language processing experience; by contrast, aptitude directly predicted both
components of vocabulary knowledge. Interestingly, strategy use was not a significant predictor of vocabulary knowledge. Lee attributed this latter finding to the
possibility that her participants (more or less advanced ones) could have used a
set of strategies selectively (hence, a lack of a significant relationship). She added
that this finding “does not imply that using or promoting L2 vocabulary strategies
is not relevant for language learning, rather it suggests that when considering the
complexity of vocabulary knowledge development, there are individual factors
that may not be as pertinent as others” (e.g., age, proficiency) (p. 12).
3. The present study
The review of the previous studies on this issue has identified some research gaps
(e.g., mostly focusing on vocabulary size, target learner populations largely being
adults). Accordingly, we include both components of vocabulary knowledge in
light of the need for a more comprehensive view of vocabulary knowledge (Lee,
2017; Schmitt, 2014) and the consideration that certain VLS may be more strongly
associated with the depth aspect of L2 vocabulary knowledge. Additionally, we
explore this issue with adolescent EFL learners whose profiles connected with the
relationships between motivation, VLS, and L2 vocabulary knowledge may differ
from those of undergraduate L2 learners since this population has been the primary target of previous research (e.g., Fan, 2020; Lee, 2020; Zhang & Lu, 2015).
In view of these considerations, the present study intends to address the following
two research questions:
1. Do motivation and VLS predict L2 vocabulary size and depth?
2. To what extent do intrinsic motivation and extrinsic motivation function
differently with VLS and L2 vocabulary knowledge?
4. Method
4.1. Participants
A total of 185 secondary-level students aged around 14 to 15 years in Seoul,
Republic of Korea, participated in this study. Among them, 78.4% (N = 145) were
8th graders and 21.6% (N = 40) were 9th graders. As for gender, 71.9% were male
442
The role of motivation and vocabulary learning strategies in L2 vocabulary knowledge: A structural. . .
(N = 133) and 28.1% were female (N = 52). At the time of the study, the 8th and
9th graders had been exposed to 506 and 626 hours of instruction in English as
a mandatory school subject, respectively (see Table 1 for the summary of participants’ demographic information). No participant had more than one month
of study abroad experience in English-speaking countries. Also, these participants could be considered EFL learners in view of their learning context (i.e.,
little exposure to the target language outside the classroom).
Table 1 Demographic information of the participants (N = 185)
Categories
Grade
Gender
Values
Numbers
Percentages
8th grade
9th grade
Male
Female
145
40
133
52
78.4%
21.6%
71.9%
28.1%
Previous hours of instruction in English
506 hours
626 hours
–
4.2. Instruments
4.2.1. Questionnaire on motivation
To measure motivation, we adapted the questionnaire used by Jang and Kim
(2014), which was based on Hayamizu’s (1997) Stepping Motivation Scale
grounded in the framework of the SDT (Deci & Ryan, 1985). It included motivational concepts, such as intrinsic reasons and external, introjected, and identified regulation. The questionnaire included items related to intrinsic motivation
(five items, e.g., “I study English because the process of increasing my English
abilities is fun”) and extrinsic motivation (comprising external, introjected, and
identified regulation; 12 items, e.g., “My parents get angry if I don’t study English” for external regulation; “I study English because I want my friends to think
of me as smart” for introjected regulation; “I think it is necessary to study English as part of my life” for identified regulation). All items were in Korean and
were measured on a 5-point Likert scale. We found that the questionnaire had
an acceptable level of reliability (α = .83 for IM and α = .77 for EM).
4.2.2. Questionnaire on vocabulary learning strategies
In terms of VLS, we adapted Park and Kim’s (2012) VLS questionnaire in Korean,
which was designed based on Schmitt’s (1997) inventory and Park’s (2001) questionnaire based on Korean EFL learners’ VLS. The finalized questionnaire comprised 23 items divided into three categories: 11 items related to memory strategies (e.g., “Connect the word to personal experience,” “Connect the word to
443
Jang Ho Lee, Joung Joo Ahn, Hansol Lee
its synonyms and antonyms”), six to cognitive strategies (e.g., “Keep a vocabulary notebook,” “Use the vocabulary section in your textbook”), and six to metacognitive strategies (e.g., “Continue to study word over time,” “Testing oneself
with word tests”). All items were in Korean and were measured on a 5-point Likert
scale. We found that the questionnaire had an acceptable level of reliability (α
= .85 for memory, α = .76 for cognitive, and α = .70 for metacognitive).
4.2.3. Vocabulary size test
Nation and Beglar’s (2007b) bilingual version of the Vocabulary Size Test (VST) was
used in consideration of the participants’ level of L2 proficiency (Nation, 2013) to
measure their vocabulary size. The VST was developed for non-native speakers of
English and covers 14,000-word families based on particular frequency levels (Nation & Beglar, 2007a), of which we used the first three (1,000, 2,000, and 3,000).
According to the Ministry of Education of Korea (2015), middle-school students are
expected to acquire around 1,250 words, which are mostly below the 2,000 level;
however, to prevent any ceiling effect, we added the 3,000 level. The test was given
in a multiple-choice format, with each correct answer carrying one point, amounting to a total of 30 points. The VST had a moderate level of reliability (α = .84).
4.2.4. Vocabulary depth test
We adapted Read’s (1993) Word Association Test (WAT) to measure the participants’ depth of vocabulary knowledge. The WAT contains 40 target adjectives,
and for each adjective eight other words are presented in a box format. In the left
and right boxes, potential synonyms and collocates of the target adjective are presented (see Figure 1 for an example). A test-taker is asked to select four words
that are related to the given target adjective, with three different combinations of
answers possible (one synonym and three collocates, two synonyms and two collocates, and three synonyms and one collocate). While adapting this test, we compared the testing words in the WAT with the English wordlist compiled for middleschool students by the Ministry of Education of Korea (2015) and selected 10 target adjectives from the given 40. Each correct answer was given one point, which
allowed for a maximum score of 40 points (10 target adjectives x four correct answers per adjective). The WAT had an acceptable level of reliability (α = .80).
1. beautiful
R enjoyable £ expensive £ free £ loud
£ education R face R music R weather
Figure 1 Sample item of word association test
444
The role of motivation and vocabulary learning strategies in L2 vocabulary knowledge: A structural. . .
4.3. Procedure
A battery of questionnaires and vocabulary tests was first piloted with 28 middleschool students, who were similar in terms of their English proficiency level and previous learning experience to the participants of the main study. The questionnaires and
tests were revised as per the students’ feedback, mostly in terms of the difficult terms
(or expressions) in the questionnaire items. For instance, the terms such as prefix and
antonym in the questionnaire items were added with some examples in English.
In the main study, the aforementioned questionnaires and vocabulary
tests were administered to the participants on two consecutive days. On the first
day, the questionnaires on motivation and VLS were given to the participants
with a time limit of 20 minutes for completion (10 minutes for each). On the
second day, the two vocabulary tests were given with a time limit of 30 minutes
(20 minutes for VST and 10 minutes for WAT). The time limits for the instruments
were fixed based on the results of the pilot. The overall procedure was conducted by the participants’ homeroom teachers in their ordinary English lessons.
4.4. Data analysis
To answer the research questions, we first collected data as following: (a) we measured learners’ intrinsic motivation and extrinsic motivation using questionnaires; (b)
we measured learners’ memory, cognitive, and metacognitive strategies using
questionnaires; and (c) we assessed learners’ L2 vocabulary knowledge using two
vocabulary tests (vocabulary size and depth tests). To ensure enough statistical
power, instead of averaging item scores for a composite score, we decided to compute latent variables for these three constructs, such as L2 motivation, L2 vocabulary learning strategies, and L2 vocabulary knowledge. Furthermore, since these
constructs were related to each other in both direct and indirect relationships based
on complex paths among them (i.e., multiple independent variables and dependent
variables in one model at the same time), we decided to simultaneously implement
a number of regression analyses. As a combination of computing latent variables
and implementing a series of regression analyses at the same time, the SEM using
Stata 16 software (StataCorp, 2019) was the primary data analysis method used in
the present study. In view of Kline’s (2012) suggestion, as well as those of other
previous studies (Jin & Lee, 2022; Lee et al., 2020, 2022), we used the following five
indices for the SEM analysis: the chi-square test (a testing model should not significantly differ from a saturated model, p > .05 for acceptable fit); the root mean
square error of approximation (RMSEA < .08 for acceptable fit); the comparative fit
index (CFI > .90 for acceptable fit); the Tucker Lewis index (TLI > .90 for acceptable
fit); and the standardized root mean square residual (SRMR < .08 for acceptable fit).
445
Jang Ho Lee, Joung Joo Ahn, Hansol Lee
5. Results
In this section, we first present the descriptive statistics of the target variables,
followed by the results of the SEM.
5.1. Descriptive statistics
Table 2 shows the mean, standard deviation, and normality test results for the
observed variables, and Table 3 shows the correlation matrix. The results indicated that all variables included in the SEM models were normally distributed.
Furthermore, the observed variables were not strongly correlated (i.e., < .60),
with the exception of the correlation between VST and WAT (r = .63, p < .001),
and that between memory and metacognitive strategies (r = .69, p < .001).
Table 2 Mean, standard deviation, and normality test results for the observed
variables (N = 185)
Variable
M(SD)
Shapiro-Wilk normality test
Vocabulary tests
VST
20.08 (4.14)
passed at 0.1%
WAT
24.63 (4.13)
passed at 5%
Motivation
IM
2.85 (0.91)
passed at 5%
EM
2.74 (.059)
passed at 5%
VLS
Memory
2.95 (0.78)
passed at 5%
Cognitive
2.96 (0.84)
passed at 5%
Metacognitive
2.75 (0.76)
passed at 5%
Note. IM = intrinsic motivation, EM = extrinsic motivation, VLS = vocabulary learning strategies, VST =
vocabulary size test, WAT = word association test.
Table 3 Correlations among the observed variables
Variable
VST
WAT
IM
EM Memory Cognitive
Metacognitive
VST
1.00
WAT
.63***
1.00
IM
.44*** .43***
1.00
EM
.31*** .39*** .48***
1.00
Memory
.52*** .49*** .57*** .46***
1.00
Cognitive
.33*** .32*** .46*** .29***
.63***
1.00
Metacognitive
.40*** .39*** .49*** .36***
.69***
.58***
1.00
Note. IM = intrinsic motivation, EM = extrinsic motivation, VST = vocabulary size test, WAT = word
association test; *** p < .001.
Before running the full SEM model in Figure 2, we checked whether all
measurement models had been measured by their indicators (variables) to ensure
446
The role of motivation and vocabulary learning strategies in L2 vocabulary knowledge: A structural. . .
construct validity. The model comprised three latent variables and seven indicators; no structural relationships were specified between the latent variables.
The results indicated that the model had acceptable model fit indices: χ2 (11) =
7.037 (p > .05), RMSEA < .08, CFI > .90, TLI > .90, and SRMR < .08. Table 4 shows
that all standardized factor loadings were statistically significant (p < .001), ranging from .60 to .90; thus, the three latent variables suggested were sufficiently
represented by their indicator variables with enough statistical power (Kline,
2012). Furthermore, the computed average variance extracted (AVE; acceptable
if ≥.50) and composite construct reliability
