research paper on vocabulary

SYSTEMATIC REVIEW article

Contribution of vocabulary knowledge to reading comprehension among chinese students: a meta-analysis.

• 1 Department of Social and Behavioural Sciences, City University of Hong Kong, Kowloon, Hong Kong
• 2 School of Economics and Management, China University of Petroleum, Qingdao, China
• 3 Faculty of Social Sciences, Southampton Business School, University of Southampton, Southampton, United Kingdom
• 4 Department of Asian Policy Studies, The Education University of Hong Kong, Tai Po, Hong Kong

This study investigated the correlation between vocabulary knowledge and reading comprehension. To address the correlation picture under Chinese logographical scripts, the researchers investigated the potential explanation for the correlation via Reading Stage, Information Gap, Content-based Approach, and Cognition and Creativity Theory approaches. This study undertook a meta-analysis to synthesize 89 independent samples from primary school stage to Master's degree stage. Results showed the correlation picture as an inverted U-shape, supporting the idea that vocabulary knowledge contributed a large proportion of variance on text comprehension and might also support the independent hypothesis of the impact of vocabulary knowledge on reading comprehension. In each education stage, the correlation between vocabulary knowledge and reading comprehension was independent in that it did not interact with any significant moderators. This study informed that the vocabulary knowledge not only determined text comprehension progress through facial semantic meaning identification but also suggested that the coordinate development of vocabulary knowledge, grammatical knowledge, and inference would be better in complexity comprehension task performance.

Introduction

Reading comprehension refers to gaining meaning from the given printed text through the interaction between readers' schema knowledge retrieval and semantic cognition ( Snow, 2002 ; Wigfield et al., 2016 ). Reading comprehension plays a vital role in two main learning perspectives—knowledge acquisition and cognition aptitude cultivation ( Perfetti and Stafura, 2014 ; Silva and Cain, 2015 ). The Simple View of Reading (SVR) posits that the fundamental knowledge for reading comprehension is vocabulary knowledge ( Hoover and Gough, 1990 ; Cromley and Azevedo, 2007 ). Vocabulary knowledge, regarded as the minimum semantic unit in reading comprehension and regarded as a component of linguistic comprehension, refers to a semantic schema on passage mental image cognition and single word or character semantic meaning identification ( Nation, 2015 ; Braze et al., 2016 ). Large vocabulary size usually represented well-structured semantic schema and better performance in word/character meaning identification. Past studies have shown that Chinese vocabulary characters, as a representor of logographic scripts, differs from alphabetical scripts in spatial structure, grammatical knowledge, and word function ( Wang et al., 2003 ; Elleman et al., 2009 ; Tong et al., 2016 ; Choi et al., 2017 ). Logographical script (e.g., Chinese characters) has a homophonic richness ( Kuo and Anderson, 2006 ), it is not always reliable in character semantic meaning identification via phonological knowledge as alphabetical words cognition. The unique feature of Chinese characters may result in a different contribution of the vocabulary knowledge to reading comprehension. From the perspective of verbal cognition development, vocabulary knowledge may contribute more on reading comprehension activities at the higher education stage (Information Gap, Katz, 2001 ). In a similar vein, learning to read transited to reading to learn will be accomplished during primary school ( Chall, 1987 ). Past studies showed that decoding contributed less variance and linguistic comprehension explained more variance in higher grades and education stage ( Mol and Bus, 2011 ; García and Cain, 2014 ). However, the effect of detailed factor (e.g., vocabulary knowledge) on reading comprehension was unknown. Whether the unique effect of Chinese character characteristics (e.g., structure) would be different from other language scripts is still unclear. Therefore, the current study aims to investigate the correlation between vocabulary knowledge and reading comprehension for Chinese readers and to further investigate the potential interaction effect between selected moderators and the association between vocabulary knowledge and reading comprehension.

Literature Review

Vocabulary knowledge and reading comprehension.

Vocabulary knowledge in reading comprehension refers to a kind of knowledge that facilitates text comprehension by single, double, or more words/characters' semantic meaning identification, providing the possibility of necessary cognitive capacity for higher-level reading processes ( Silva and Cain, 2015 ; LervAag et al., 2018 ). Extant literature has shown that vocabulary knowledge contributes to reading comprehension through semantic meaning identification and played a collaborator role with inference on sentence meaning comprehension ( Silva and Cain, 2015 ; LervAag et al., 2018 ; Lawrence et al., 2019 ). High quality of word semantic meaning identification is beneficial for accurate individual word meaning retrieval ( Perfetti and Hart, 2002 ), which establishes word-and-word unit for sentence proposition coherence ( Cain et al., 2004 ; Braze et al., 2016 ). Past evidence has shown that vocabulary is significantly related to inference ability, listening comprehension, and reading comprehension ( Lepola et al., 2012 ; Cain and Oakhill, 2014 ; Daugaard et al., 2017 ). Chinese is a kind of logographic script that is different from alphabetical script (e.g., English) in character construction ( Ku and Anderson, 2003 ; Ramirez et al., 2010 ), grammatical knowledge ( Bawa and Watson, 2017 ; Paradis and Jia, 2017 ), and function words sequence ( Chen et al., 2016 ; Lee et al., 2017 ). Chinese characters are usually constructed by two components: the radical part usually represents the pronunciation of the character; the other side of the component represents the function of the character. The structure usually could be divided into three categories: left-right (e.g., 棋), top-down (e.g., 盛), and surround (e.g., 困). In Chinese, the restricted semantic components (e.g., time, objects, and status of the subjects) are usually inserted into the sentences rather than set at the end of the sentence or an independent component at the first part in the sentence. In particular, a single character could also be one sentence with a complete meaning [e.g., 懂 (dǒng) represents the meaning of someone understanding the whole meaning, skills, or the content that the other one mentioned]. The function and the meaning of the Chinese character are determined by the semantic meaning situation. For example, “败 (bài)” could be a verb (i.e., beat) or an adjective (lose). In the sentence “A败B,” the meaning of “败” could be win or lose; if the sentence situation shows “A” has advantages, the meaning should be win; otherwise, the meaning could be lost. Chinese characters have an omit function; the four-character idiom could represent great semantic meaning (e.g., “博大精深” represents the subject holds a great history/knowledge based on the current dialogue topic). Vocabulary knowledge contributed to reading comprehension through word recognition directly (e.g., Mezynski, 1983 ; McBride-Chang et al., 2005a ) and through reading fluency, decoding ability, and reading rate indirectly ( Hilton, 2008 ; Spencer and Wagner, 2018 ). Past studies showed that vocabulary knowledge contributed to reading comprehension process via word semantic meaning recall (semantic feature of orthographic, morphological, phonological, and pragmatic characteristics) speed and quality to achieve a mental image from the given text ( Perfetti, 1985 ; Logan and Kieffer, 2017 ; Lawrence et al., 2019 ). However, the inconsistent results of various correlations between vocabulary knowledge and reading comprehension have been found in Chinese students, from low correlation (e.g., Cheng et al., 2017 ) to high correlation (e.g., Li et al., 2009 ). The unique effect of vocabulary knowledge on reading comprehension remains unknown among Chinese students; therefore, the role of the vocabulary knowledge effect on reading comprehension for Chinese participants requires further investigation.

Potential Moderators Selection

The current study selects grade group, education stage, language type, and sampling area as potential moderators. Reasons are listed below.

Grade Group

Reading stage statement ( Chall, 1987 ) showed that grade group would be a potential moderator on the association between vocabulary knowledge and reading comprehension. The statement showed that readers started learning to read at lower grades of the primary school and transition to reading to learn at higher grades of primary school. The higher reading stages matched higher reading cognition ability, which may have interacted with the association between vocabulary knowledge, and reading comprehension.

Education Stage

From the perspective of the task-oriented requirement, the Information Gap Theory ( Katz, 2001 ) suggested that education stage—from primary school stage to Master's stage—would be a potential moderator on the association between vocabulary knowledge and reading comprehension. The higher education stage provided the higher requirement of reading comprehension tasks in word cognition, passage structure cognition, and passage main idea identification. The higher requirement of the reading comprehension task may result in a higher association between vocabulary knowledge and reading comprehension.

Empirically, grade group has been shown to have a close relationship with decoding ability, which serves as a determination factor in vocabulary knowledge (e.g., morphological knowledge on radical component meaning identification). Past studies have already shown that the association between decoding ability and reading comprehension decreased by grade group (e.g., Mol and Bus, 2011 ; García and Cain, 2014 ). According to the reading stage statement and the information gap statement on reading, the current study divided grade group into two groups. Regarding the reading stage statement, grades 1–6 of primary school were divided into lower grades of primary school, grades 1 and 2; middle grades of primary school, grades 3 and 4; and higher grades of primary school, grades 5 and 6. According to the information gap statement of reading, this study used education stage (PS: primary school, SS: secondary school, US: undergraduate stage, MS: Master's stage) to represent different grade groups.

Language Type

Content-based Approaches ( Cloud et al., 2000 ) suggested that verbal cognition difficulty negatively correlated with the association between vocabulary knowledge and reading comprehension across different language scripts for readers. Past studies showed that the cognition difficulty was higher in second-language (L2) than in first-language (L1) scripts. In addition, it was confirmed that morphological knowledge made a higher contribution to logographic scripts cognition than phonological knowledge ( Yeung et al., 2011 ; Ruan et al., 2018 ). In contrast, phonological knowledge made a higher contribution to alphabetical scripts cognition than to logographical scripts ( Seidenberg, 2011 ). The current study selected Chinese students as participants; thus, the cognition difficulty might be higher in alphabetical scripts comprehension than in logographical scripts comprehension. Therefore, the language type may interact with the association between vocabulary knowledge and reading comprehension.

Sampling Area

Cognition and Creativity Theory ( Runco, 2007 ) suggested that verbal ability application in reading comprehension was affected by visual and auditory cognition. Mainland China, Hong Kong, and Taiwan have different writing systems and oral language systems in Chinese academic studies (e.g., Siok and Fletcher, 2001 ; McBride-Chang et al., 2005b ). Regarding the writing system, mainland China uses a simplified script while both Hong Kong and Taiwan use traditional script. The differences mainly come from the number of strokes (the simplified version has ~22.5% fewer strokes than the traditional version has) and characters' structure complexity (traditional script is more complex). In addition, the pronunciation, grammatical knowledge, and sentence construction are very different between Mandarin (used in mainland China and Taiwan) and Cantonese (used in Hong Kong). The complexity of words impacts reading comprehension performance ( Filippi et al., 2015 ; LervAag et al., 2018 ).

Relevant Meta-Analysis Studies Between Vocabulary and Reading Comprehension

In the last three decades, a few studies investigated the effect of vocabulary knowledge on reading comprehension. These mainly adopt two mainstream approaches to synthesize the effect size between vocabulary knowledge and reading comprehension. The majority of studies focus on vocabulary knowledge intervention effect on reading comprehension (e.g., Elleman et al., 2009 ; Marulis and Neuman, 2010 ; Dexter and Hughes, 2011 ), providing each effect size for specific intervention programs. The second group reflects the correlation between vocabulary knowledge and reading comprehension. However, past correlational meta-analytic studies have three main limitations. First, such studies (e.g., Jeon and Yamashita, 2014 ) only included a small number of empirical studies, which may not represent the real correlation between vocabulary knowledge and reading comprehension. In addition, the study by Jeon and Yamashita (2014) did not provide any convincing association results, because the heterogeneity problem and the outliers were not removed. Second, past studies show limitations in participants' selection. For example, Kudo et al. (2015) reported the correlation between vocabulary knowledge and reading comprehension in readers with learning difficulties only. Finally, a few studies provided the correlation picture on logographical scripts' characters in which semantic meaning could be defined via morphemes.

The Current Study

The current study investigates the picture between vocabulary knowledge and reading comprehension for Chinese students from primary education stage to Master's education stage. Specifically, this study investigates the possible interaction effect explanations for the association between reading comprehension and vocabulary knowledge in Chinese readers from the reading stage, information gap, content-based approaches, and cognition and creativity theory perspectives. Moreover, the interaction effect of education stage, grade group, language type, and sampling area with the association between vocabulary knowledge and reading comprehension is also examined. Under the guidelines of PRISMA , the current study selects the most recent 20 years of empirical studies as materials, investigating the correlation between vocabulary knowledge and reading comprehension in Chinese students.

Literature Base

This study selected potential materials from different databases. To avoid any misunderstanding of the scripts, the authors selected the materials written in Chinese and English only. The Chinese materials were selected from the CNKI database, which included all possible academic empirical studies written in Chinese. Empirical studies written in English were selected from PsycINFO, ERIC, and Pro-Quest Dissertations and Theses. Two groups of key terms were used to search the empirical studies. Group 1 refers to vocabulary knowledge, including vocabulary * , vocabulary knowledge * , breadth of vocabulary * , and depth of vocabulary * . The second group refers to reading comprehension, including sentence comprehension * , paragraph comprehension * , passage comprehension * , text comprehension * , reading ability * , reading performance * , and comprehension * . All searched materials were published in the last 20 years (1998–2018).

Inclusion Criteria

All selected empirical studies (articles, dissertations, and conference paper) have to meet all the following criteria: (a) sample size over 30; (b) empirical studies and non-opinion studies; (c) provided exact reading comprehension scores; (d) participants were Chinese students; (f) Chinese was L1 for participants; (g) reading comprehension measurement reported sentence comprehension scores or passage comprehension scores; and (h) provided enough indicators for effect size calculation. Regarding correlation indicator, this study included correlation ( r ) and percentage of variance ( R 2 ) in reading comprehension accounted for by vocabulary knowledge.

In addition, those studies with composite measurement of reading skills (e.g., vocabulary plus reading comprehension and reading plus listening comprehension) were removed in order to ensure that the effect size only reflected the correlation between reading comprehension and vocabulary knowledge. Moreover, both vocabulary knowledge and reading comprehension should be measured at the same time from the same sample because the current study tries to report the concurrent correlation between vocabulary knowledge and reading comprehension. Detailed information of potential studies search was provided in Figure 1 .

Figure 1 . Flow chart for material selection.

Coding Process

Two coders coded the following information independently: (a) year of publication, (b) first author, (c) sampling area, (d) sample size, (e) grade group, (f) education stage, (g) language type, and (h) effect size of the correlation between vocabulary knowledge and reading comprehension. If the data were absent from the original materials, the coders emailed the authors for information. Two coders removed those articles in which these eight key items were unclear.

If the selected article's participants were primary school students, to address the hypothesis of the interaction effect of the reading stage on the correlation between vocabulary knowledge and reading comprehension, the authors separated the studies as independent samples if participants came from different grade groups. To investigate the interaction effect of language type on the association between vocabulary knowledge and reading comprehension, the authors separated the studies as independent samples if one article provided the following two correlations—the first one was between L1 vocabulary knowledge and L1 reading comprehension, and the second one was between L2 vocabulary knowledge and L2 reading comprehension. This study removed those correlation effect sizes where the vocabulary knowledge and reading comprehension came from different language scripts, specifically the effect size between L1 vocabulary knowledge and L2 reading comprehension and the effect size between L2 vocabulary knowledge and L1 reading comprehension. Otherwise, if one article provided more than one available effect size, they were subjected to robust variance estimation ( Hedges et al., 2010 ) for effect size estimation, ensuring that each independent sample only provided one effect size for further meta-analysis. The intercoder agreement for both study characteristics and outcome variables was 95% across meta-analyses, and all discrepancies between coders came from the sampling area. The authors solved this problem by removing those articles in which the sampling area was mixed—for example, the participants came from both mainland China and Hong Kong and the correlation effect size was not clear for either sampling area.

Meta-Analytic Procedures

This study followed standard analytic procedures as claimed in PRISMA ( Moher et al., 2011 ). All correlation indicators were entered into Comprehensive Meta-analysis for Fisher's z calculation. This study selected Fisher's z because z followed asymmetrical distribution ( Borenstein et al., 2009 ). To interpret the effect size, the values of Fisher's z were 10, 31, and 55, to be interpreted as small effect size, moderate effect size, and large effect size, respectively ( Cohen, 1988 ).

To be conservative, this study applied indicators from the random-effect model, which includes the value of Fisher's z , variance, Q -value, and 95% confidence interval (CI). Fisher's z could be interpreted as significant when 95% CIs do not cross zero ( Hedges and Pigott, 2004 ). Then, meta-regression was applied for moderator analysis when Q reached a level of significance. This study also examined sensitivity analysis through randomly removing one sample from the list. Furthermore, Orwin's safe number, funnel plot through trim-and-fill approach, p -value of Begg's rank correlation test, and Egger's regression intercept test were reported to address publication bias.

To compare the effect sizes between each group, the authors calculated δ for further analysis: δ = Diff / SE, Diff = Fisher's z 1 – Fisher's z 2 , SE = Sqrt (Variance z 1 + Variance z 2 ), if |δ| ≥ 1.96. They interpret the result to have significant difference ( p < 0.05).

Descriptive Statistics

Detailed information of selected studies were shown in Table 1 . Three outliers from primary school grades' list were removed due to an effect size of over 3.5 standard deviation ( García and Cain, 2014 ): Cheng and Wu (2017) from the lower primary grades' list and Chen (2015) and Chen at al. (2018) from the higher primary grades' list. The remaining 81 studies included in the meta-analysis represented a total of 10,668 participants obtained from 89 independent samples. Of these, 29 samples ( n = 4,672) reported the correlation between vocabulary knowledge and reading comprehension for primary school students. In particular, 17 samples ( n = 2,400) reported the correlation in lower primary grades, 6 samples ( n = 1,019) reported the correlation in middle primary grades, and 6 samples ( n = 1,253) reported the correlation in higher primary grades. Furthermore, 21 samples ( n = 3,122) reported the correlation between L1 vocabulary knowledge and L1 reading comprehension, and 8 samples ( n = 1,550) reported the correlation between L2 vocabulary knowledge and L2 reading comprehension.

Table 1 . Descriptive information of the selected studies.

Eleven (11) samples ( n = 850) reported the correlation effect size in secondary school students. All 11 samples reported the correlation between L2 vocabulary knowledge and L2 reading comprehension. Next, 45 samples ( n = 4,506) reported the correlation effect size in undergraduate students. All 45 samples reported the correlation between L2 vocabulary knowledge and L2 reading comprehension. Four samples ( n = 640) reported the correlation in Master's students. All four samples reported the correlation between L2 vocabulary knowledge and L2 reading comprehension.

Eleven (11) samples ( n = 1,938) reported Hong Kong students' correlation between vocabulary knowledge and reading comprehension. A further 72 samples ( n = 7,914) reported mainland China students' correlation between vocabulary knowledge and reading comprehension. Four samples ( n = 517) reported the correlation between vocabulary knowledge and reading comprehension for those Chinese students who lived in other countries. Five samples ( n = 776) reported Taiwan students' correlation between vocabulary knowledge and reading comprehension.

Meta-Analysis

As shown in Table 2 the overall correlation effect size between vocabulary knowledge and reading comprehension was nearly large ( z = 0.54, p < 0.001). The Q -value was significant ( Q = 204.61, p < 0.001). Moderator analysis showed that the education stage explained 66% ( p < 0.001) of the variance, and the sampling area explained 10% ( p < 0.01) of the variance. Language type did not have a significant interaction effect with the correlation between vocabulary knowledge and reading comprehension for Chinese participants.

Table 2 . Meta-analysis.

To further address the hypothesis from the Information Gap statement and the Reading Stage statement, following the application of data-driven approach under the guidance of PRISMA , the authors further examined the correlation between vocabulary knowledge and reading comprehension in each education stage through heterogeneity analysis. Regarding primary school, the effect size was 50 ( p < 0.001) and the Q -value was 34.84 ( p > 0.10, I 2 = 19.64). The publication bias test showed that Orwin's fail-safe number was 259, the Tau value for Begg's rank correlation test was 03 ( p > 0.10), and Egger's regression intercept was 49 ( p > 0.10). The funnel plot showed that effect size had a symmetry distribution ( Figure 2 ), indicating that the correlation effect size for primary school students did not have significant publication bias. Results suggested that reading stage statement did not have a significant interaction effect with the correlation between vocabulary knowledge and reading comprehension in primary school. Regarding sensitivity analysis, the authors randomly removed one study from the list. The result was similar, indicating that the results had higher reliability.

Figure 2 . Funnel plot of the correlation effect size between vocabulary knowledge and reading comprehension for primary school students.

Regarding secondary school, the effect size was 74 ( p < 0.001) and the Q -value was 4.18 ( p > 0.10, I 2 < 0.001). The publication bias test showed that Orwin's fail-safe number was 153, the Tau value for Begg's rank correlation test was 22 ( p > 0.10), and Egger's regression intercept was 71 ( p > 0.10). The funnel plot showed that effect size had a symmetric distribution ( Figure 3 ), indicating that the correlation effect size for secondary school students did not have significant publication bias. Regarding sensitivity analysis, the authors randomly removed one study from the list. The result was similar, indicating that the results had higher reliability.

Figure 3 . Funnel plot of the correlation effect size between vocabulary knowledge and reading comprehension for secondary school students.

Regarding undergraduate students, the effect size was 55 ( p < 0.001) and the Q -value was 39.97 ( p > 0.10, I 2 < 0.001). The publication bias test showed that Orwin's fail-safe number was 447, the Tau value for Begg's rank correlation test was 17 ( p > 0.10), and Egger's regression intercept was 76 ( p > 0.10). The funnel plot showed that effect size had a symmetric distribution ( Figure 4 ), indicating that the correlation effect size for undergraduate students did not have significant publication bias. Regarding sensitivity analysis, the authors randomly removed one study from the list. The result was similar, indicating that the results had higher reliability.

Figure 4 . Funnel plot of the correlation effect size between vocabulary knowledge and reading comprehension for undergraduate students.

Regarding Master's students, the effect size was 28 ( p < 0.001) and the Q -value was 1.77 ( p > 0.10, I 2 < 0.001). The publication bias test showed that Orwin's fail-safe number was 19, the Tau value for Begg's rank correlation test was 60 ( p > 0.10), and Egger's regression intercept was 6.37 ( p > 0.10). The funnel plot showed that effect size had a symmetric distribution ( Figure 5 ), indicating that the correlation effect size for Master's students did not have significant publication bias. Regarding sensitivity analysis, the authors randomly removed one study from the list. The result was similar, indicating that the results had higher reliability.

Figure 5 . Funnel plot of the correlation effect size between vocabulary knowledge and reading comprehension for Master students.

Effect Size Comparison

The effect size of primary school was significantly lower than the effect size of secondary school (|δ| = 5.68, p < 0.001), the effect size between primary school and undergraduate was not significant (|δ| = 1.34, p > 0.10), and the effect size of primary school was significantly higher than the effect size of Master's students (|δ| = 5.51, p < 0.001). The effect size of secondary school was significantly higher than the effect size of undergraduate students (|δ| = 5.08, p < 0.001), and the effect size of secondary school was significantly higher than the effect size of Master's students (|δ| = 5.69, p < 0.001). The effect size of undergraduate students was significantly higher than the effect size of Master's students (δ| = 6.36, p < 0.001).

This study synthesized 89 independent samples to investigate the correlations between vocabulary and reading comprehension in Chinese readers from primary school stage to Master's stage. The overall correlation effect size was nearly large. The result is consistent with previous survey studies that have shown that vocabulary knowledge had great variance in explaining the mental image construction process via verbal cognition and semantic identification ( Cain et al., 2004 ; Quinn et al., 2015 ; Gottardo et al., 2018 ). For example, vocabulary knowledge provides different potential semantic meanings of the target word or characters to assist readers' cognition of the adjacent coherence between words and sentences ( Prior et al., 2014 ; Perfetti, 2017 ).

The correlation effect size was moderated significantly by education stage. Results showed that the interaction effect of grade group, language type, and sampling area was not significant, rejecting the possible interaction impact from the reading stage, content-based approach, and cognition and creativity statements via the link between vocabulary knowledge and reading comprehension. The correlation picture was an inverted U-shape from primary school stage to Master's stage. The tendency of the correlation was consistent with those cross-sectional studies with multiple grade groups (e.g., Chik et al., 2012 ) and longitudinal studies for different grade group performance surveys ( Zhang et al., 2012 ; Siu and Ho, 2015 ; Cheng et al., 2016 ). There are three possible explanations on the significant interaction effect between education stage and the association of vocabulary knowledge and reading comprehension. Firstly, vocabulary knowledge might have an independent contribution on the reading comprehension. Previous studies argued that vocabulary knowledge contributed to reading comprehension directly due to the derived meaning of vocabulary on the mental representation construction ( Ouellette and Beers, 2010 ; Tunmer and Chapman, 2012 ). Chinese readers tend to identify the semantic meaning of characters or words from morphological and orthographical coding than phonological coding (e.g., Dong et al., 2019 ); for example, readers tend to identify the function of the character through the radical component of characters and then ensure the pronunciation from the rest of the components, which may not determine the identifying facial and deep mental lexical meaning from the given text. Text comprehension progress relies more on semantic meaning identification on each character rather than on accurate pronunciation of the character. Semantic meaning, especially the facial semantic meaning from the given text cognition, determined the readers' mental image construction via the final global inference. Moreover, vocabulary knowledge directly impacted the process of target character or word decoding progress ( Ouellette and Beers, 2010 ; Tunmer and Chapman, 2012 ), indicating that the vocabulary knowledge was an independent variable on reading comprehension cognition, which does not belong to decoding and linguistic comprehension ( The Simple View of Reading : Hoover and Gough, 1990 ). Past studies confirmed that the association between decoding and comprehension decreased when the grade group increased ( Mol and Bus, 2011 ; García and Cain, 2014 ); therefore, the proportion of linguistic comprehension contribution on reading comprehension should be increased. However, the current results partially match the development of linguistic comprehension, which might provide evidence for the independent effect of vocabulary knowledge development on reading comprehension. The fact that Chinese characters could be identified by the structure from students' schema could be an alternative reason. School curricular syllabus required students to enlarge vocabulary size from primary school to secondary school. Students learn new characters through retrieval decoding skills and schema knowledge and through recognizing familiar radical components and comparing the target character with previous acquired relevant characters' information; therefore, the increasing knowledge of vocabulary would have more effect on reading comprehension activities. However, since the stage of higher education, syllabus required less on students' vocabulary knowledge development but required more on students' grammatical and inference ability application; therefore, the speed and size of the vocabulary schema cognition construction development would be lower, resulting in less contribution on reading comprehension than primary and secondary education stage. Corresponding with the syllabus requirement, the interaction effect between complicated reading task in higher grade groups and the reading schema for semantic knowledge retrieval would be the third reason. Vocabulary knowledge contributed to comprehension progress via character semantic meaning identification and especially worked on facial meaning identification. From primary school to secondary school, the requirement of reading comprehension was an examination of the reading ability; the larger vocabulary knowledge base contributed to faster semantic knowledge retrieval ( Wolf et al., 2000 ; Ecke, 2015 ). At the same time, the assessment of the reading comprehension task was not complicated. After graduating from secondary school, the reading knowledge schema assisted readers to imagine the mental representation from the given text. At the same time, the more complicated passage structure cognition process needed more reading knowledge (e.g., reading strategy, higher-order thinking) collaboration. When these were combined, the contribution proportion of the vocabulary knowledge decreased. For example, text reading comprehension not only needs word recognition but also needs a combination of strategies, inference ability, and other relevant factors (e.g., linguistic knowledge) to do text cognition, thereby leading to smaller correlation in higher-grade groups. An alternative reason could be reading comprehension difficulties. Readers might experience problems on global or adjacent text coherence cognition even though each word or character's meaning was well-identified ( Oakan et al., 1971 ; Catts et al., 2016 ). The large effect size between vocabulary knowledge and reading comprehension informed vocabulary knowledge preliminarily provided the facial meaning on target character/word semantic identification and determined comprehension activity progress. At any education stage, the curricular design should pay more attention to students' vocabulary schema development. Moreover, due to the complexity comprehension activity requirement, schools should remind students to develop vocabulary knowledge with grammatical and inference ability coordinately on comprehension task performance, enhancing mental image construction via well-constructed deep semantic meaning.

Limitations and Implications

The current study has four main limitations. First, previous studies reported that vocabulary might have an independent contribution to reading comprehension directly rather through decoding and linguistic comprehension ( Ouellette and Beers, 2010 ; Tunmer and Chapman, 2012 ); the current study results did not fully support this statement through simple meta-analytic approach. For future studies, a network meta-analytical approach may be a reliable approach to investigate the effect. Second, the current study only examined the interaction effect on the association between vocabulary knowledge and reading comprehension from grade or education stage, language type, and sampling area; the other factors' effect [e.g., text comprehension level ( Sparks et al., 2008 )] was not included. Third, it did not investigate the interaction effect within selected moderators. Finally, from secondary school stage to Master's stage, all selected studies reported Chinese students' correlation between L2 vocabulary knowledge and L2 reading comprehension only.

The results of the current study indicated the correlation between reading comprehension and vocabulary for Chinese participants, and age or education stage should be considered as a key variable to control due to the significant interaction effect with the target correlation. Second, for those intervention designs that aim to improve reading comprehension through a vocabulary intervention program, the appropriate time for higher intervention effect size should be during primary school, secondary school, and undergraduate stage. Finally, regarding teaching activities, because the contribution of vocabulary on reading comprehension decreased since secondary school, teaching activities should pay more attention to other linguistic factors' (e.g., inference) design during the school reading program.

This study found the inverted U-shape correlation picture between vocabulary knowledge and reading comprehension in Chinese participants. Results showed that vocabulary knowledge might have an independent effect on reading comprehension in each education stage, which rejected the possible interaction effect of grade group in primary school, sampling area, and language type in different script cognition. Results showed that the correlation effect size decreased since secondary school education stage, the reason being the higher difficult level of text comprehension, which suggested that other higher-order thinking factors (e.g., inference) may contribute a higher proportion on text comprehension.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author Yi Tang.

Author Contributions

YD drafted the most part of the manuscript and did data analysis. YT revised the manuscripts and did data analysis. BW-YC provided critical comments to the draft. WW and W-YD helped data collection and provided comments to the draft. All authors contributed to the article and approved the submitted version.

The work described in this paper was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (CityU 11619816).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Zou, Q. M., and Guo, J, J. (2008). Factors affecting incidental vocabulary acquisition in english reading. J. Guangdong Univ. Foreign Stud. 19, 97–100.

Keywords: vocabulary knowledge, reading comprehension, reading stage, education stage, information gap, Chinese students

Citation: Dong Y, Tang Y, Chow BW-Y, Wang W and Dong W-Y (2020) Contribution of Vocabulary Knowledge to Reading Comprehension Among Chinese Students: A Meta-Analysis. Front. Psychol. 11:525369. doi: 10.3389/fpsyg.2020.525369

Received: 09 January 2020; Accepted: 19 August 2020; Published: 02 October 2020.

Reviewed by:

Copyright © 2020 Dong, Tang, Chow, Wang and Dong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yi Tang, t632941475@163.com ; Bonnie Wing-Yin Chow, wychow@cityu.edu.hk

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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The Influence of Reading on Vocabulary Growth: A Case for a Matthew Effect

a University of Iowa, Iowa City

J. Bruce Tomblin

b Florida State University, Tallahassee

Individual differences in vocabulary development may affect academic or social opportunities. It has been proposed that individual differences in word reading could affect the rate of vocabulary growth, mediated by the amount of reading experience, a process referred to as a Matthew effect ( Stanovich, 1986 ).

In the current study, assessments of written word–reading skills in the 4th grade and oral vocabulary knowledge collected in kindergarten and in the 4th, 8th, and 10th grades from a large epidemiologically based sample ( n = 485) allowed a test of the relationship of early word-reading skills and the subsequent rate of vocabulary growth.

Consistent with the hypothesis, multilevel modeling revealed the rate of vocabulary growth after the 4th grade to be significantly related to 4th-grade word reading after controlling for kindergarten vocabulary level, that is, above average readers experienced a higher rate of vocabulary growth than did average readers.

Conclusions

Vocabulary growth rate differences accumulated over time such that the effect on vocabulary size was large.

There are large differences between individual children in their vocabulary knowledge on school entry (e.g., Hart & Risley, 1995 ), and these differences in vocabulary extend into the school years. For example, Biemiller and Slomin (2001) reported that in the second grade, children at the lowest quartile for vocabulary had approximately half the number of known words compared to students in the top quartile. Furthermore, according to the Matthew effect model proposed by Stanovich (1986 , 2000) , those individual differences in vocabulary may even increase over time. The term Matthew effect refers to a biblical text and was originally proposed to describe the progress of scientific research careers ( Merton, 1968 ) in which advantages and disadvantages accumulate, so that the rich get richer and the poor get poorer. In terms of reading, the general premise of the Matthew effect model is that individual differences in reading skill (broadly conceived) could accumulate over time ( Stanovich, 1986 , 2000 ) so that a child's initial reading level would be positively related to his or her rate of growth in a reading skill. This pattern, in which growth rates differ across skill levels even while absolute skill levels increase for all, is considered a relative Matthew effect ( Rigney, 2010 ). Accumulating advantages and disadvantages, of course, are only one possible developmental pattern. A compensatory model would predict that initial reading level would be negatively related to rate of growth in reading skill so that differences in reading skill would decrease over time, effectively the opposite of a Matthew effect ( Pfost, Hattie, Dorfler, & Artelt, 2014 ). A third possibility would be a stable achievement pattern, with high and low skill readers having the same rates of growth over development ( Pfost et al., 2014 ).

This study concerns one specific prediction of the Matthew effect model, namely, that reading skill in general, and word reading skill in particular, could be related to the rate of vocabulary growth. Vocabulary skill is strongly related to a variety of academic, vocational, and social outcomes ( Dollinger, Matyja, & Huber, 2008 ; Gertner, Rice, & Hadley, 1994 ; Rohde & Thompson, 2007 ). The veracity of this prediction of the Matthew effect model is significant because it could help guide interventions for children at risk of poor vocabulary development. The current study includes children sampled from a large epidemiologic study, which includes children with language impairments and cognitive impairments.

The prediction that reading skill could be associated with rate of vocabulary growth is based on the premise that reading development could potentially have a significant effect on a child's exposure to novel words. In fact, there is empirical evidence that, for older children and adults, much learning of new words occurs through exposure to written texts ( Nagy, Herman, & Anderson, 1985 ; Sternberg, 1987 ). Because print material generally contains many more low frequency words than does spoken language ( Cunningham, 2005 ), reading text can provide key opportunities for advancement in vocabulary development. We predict that word learning through reading will affect vocabulary as measured on both oral and written tasks because words learned through reading text will be at least partially available to the individual for both written and oral language use ( Nelson, Michal, & Perfetti, 2005 ).

However, exposure to novel words in text does not occur uniformly throughout reading development. Prior to formal literacy instruction, children are clearly acquiring novel vocabulary through exposure to oral language. During early reading development, children rarely confront words in print that are not already present in their vocabulary, so much of the lexical knowledge of words, especially phonological and semantic representations, will be derived from oral language experience. As children become more proficient readers and advance to more complex print material, they are more likely to confront words during reading that they have not been exposed to via listening. This transition likely occurs around the third or fourth grade for many students ( Chall, 1987 ). Biemiller (2005) , for example, reported that, from the third grade onward, but not in earlier grades, 95% of children could read more words than they could explain.

The Existence of a Matthew Effect for Vocabulary

According to one of the predictions of the Matthew effect model, vocabulary development after the third or fourth grade would be affected by reading ability and the associated reading experiences enabled by these reading skills. This study investigates a rather straightforward prediction with respect to vocabulary development and reading during the middle grades and high school. We predict that better readers during this time will have a greater likelihood of confronting novel, low-frequency words than will weak readers and that this will affect the rate of vocabulary growth. This prediction is predicated on the notion that strong readers will engage in more reading activities than will weak readers. This assumption is consistent with Stanovich's (1986) proposal that the volume of reading experience is the key mediating variable between reading skill (broadly conceived) and vocabulary, with cumulative advantages occurring due to “the effect of reading volume on vocabulary growth, combined with large skill differences in reading volume” (p. 381). There is empirical evidence to support the assumption that reading skill and the amount of reading experience are strongly associated. For example, Allington (1983) reported that strong 1st-grade readers read three times as many words during reading instruction as do weak readers. Nagy and Anderson (1984) suggested that a motivated middle-school student might read 100 times more words a year in the classroom than a less skilled or motivated student. With respect to reading for pleasure, Juel (1988) reported that average and strong readers in the third and fourth grades read at home more times per week than did weak readers, and Martin-Chang and Gould (2008) reported correlations between reading speed (words per minute) and personal reading experience in undergraduate students.

A small number of studies have previously investigated a Matthew effect with vocabulary as an outcome variable. Aarnoutse and van Leeuwe (2000) reported that weak readers showed larger effect sizes in vocabulary growth than did strong readers in early elementary grades, thus leading the authors to question a Matthew effect of reading on vocabulary. Vocabulary was measured in a written format; thus, reading ability could have confounded the measure of vocabulary. In contrast, Cain and Oakhill (2011) reported that readers who had weak reading comprehension skills showed lower rates of vocabulary growth between the ages of 8 and 16, compared with good comprehenders, and concluded that there was a Matthew effect for reading skill on vocabulary. In this case, vocabulary was measured via both word reading and listening vocabulary. In a similar manner, Kempe, Eriksson-Gustavsson, and Samuelsson (2011) reported evidence of a Matthew effect on the growth of vocabulary in the 1st to third grades, as measured orally using the Wechsler Intelligence Scale for Children–Third Edition ( Wechsler, 1991 ). In addition, Stothard, Snowling, Bishop, Chipchase, and Kaplan (1998) reported a decrease in scores on the British Vocabulary Scale ( Dunn, Dunn, Whetton, & Pintilie, 1982 ) between ages 8 and 15 for children who had been classified as having persistent specific language impairment and general delay, but not for children whose language was within the expected range or for children whose early language concerns had resolved by age 5 years. None of these studies used developmental scaling to equate item difficulty across different age groups. Further, none of the afore-mentioned studies controlled for rate of vocabulary learning prior to literacy instruction. It is reasonable to expect that the various factors that contribute to these individual differences in word learning in early life might continue to exert effects on word learning when reading. There are, in fact, substantial differences in word-learning achievement in prereaders (e.g., Hart & Risley, 1995 ), which would affect the level of vocabulary knowledge when children start to learn new words through written language. Furthermore, these individual differences in word-learning skills would be expected to covary with reading skill, given the substantial overlap between disorders of word reading and of language skills ( Catts, Adlof, Hogan, & Ellis Weismer, 2005 ). In order to examine the specific effect of reading experience on vocabulary, it would seem wise to control for the child's general word-learning achievement. Thus, the evidence for a Matthew effect on vocabulary is mixed and is possibly confounded by word-learning abilities in general.

The above discussion concerns the effect of reading skill on vocabulary growth, which is only one prediction of the Matthew effect model. Other predictions of the model have also been tested, with similarly equivocal results ( Pfost et al., 2014 ). Some studies report data that support a Matthew effect for reading ability ( Juel, 1988 ), but others report a stable achievement pattern ( Aarnoutse & van Leeuwe, 2000 ; Catts, Adlof, & Fey, 2003 ; Scarborough & Parker, 2003 ; Shaywitz et al., 1995 ) or a compensatory effect ( Parrila, Auonola, Leskinen, Nurmi, & Kirby, 2005 ; Shaywitz et al., 1995 ). The diversity of findings in these studies is undoubtedly related to the wide variety of outcome variables and ages of readers as well as to the characteristics of the sample group and study methodologies. Indeed, some studies do report different conclusions on the basis of the outcome variable studied ( Bast & Reitsma, 1998 ; Shaywitz et al., 1995 ), the subgroup of children looked at ( Jacobson, 1999 ; Morgan, Farkas, & Hibel, 2008 ; Phillips, Norris, Osmond, & Maynard, 2002 ; Stothard et al., 1998 ), and even the language in which children were learning to read ( Parrila et al., 2005 ). In addition, a recent meta-analysis ( Pfost et al., 2014 ) concluded that the psychometric properties of the measures were also important: studies using measures without floor or ceiling effects and with good reliability were more likely to report the presence of a Matthew effect. As a final consideration, the populations studied may have differed in amount or kind of intervention received. Hence, although the Matthew effect model has been a very helpful framework for researchers, educators, and clinicians alike, evidence for it has remained elusive ( Pfost et al., 2014 ; Scarborough & Parker, 2003 ).

Where a Matthew effect is reported, there is more than one possible pattern because the effect of initial reading skill on subsequent growth rates may not necessarily be the same across the continuum of reading skill ( Protopapas, Sideridis, Mouzaki, & Simos, 2011 ; Rigney, 2010 ). On the one hand, strong readers might show increasing gains relative to average readers at the same time as weak readers show decreasing gains relative to average readers. We refer to this as a two-sided Matthew effect . On the other hand, strong readers could show increasing gains relative to average readers, whereas weak readers have gains similar in size to those of average readers. The reverse of this pattern is also possible in which weak readers show slower growth rates than average readers without strong readers showing faster growth rates (e.g., Morgan et al., 2008 ). These last two possibilities have been termed one-sided Matthew effects ( Morgan et al., 2008 ), and we describe them as such.

It is clear that the selection of outcome and predictor variables is of critical importance in tests of a Matthew effect. Stanovich's (1986) proposal about reading and vocabulary considered reading in a broad sense. In this study, word reading (of nonwords and single words) is used to operationalize reading skill. The rationale for using word-reading skill as a predictor variable is simply that is expected to be less confounded with vocabulary than reading comprehension scores would be because reading comprehension and vocabulary scores are highly correlated (e.g., Pearson, Hiebert, & Kamil, 2007 ). The use of word-reading scores therefore allows for a clearer interpretation of the data. Likewise, vocabulary can be defined in different ways, including across receptive and expressive dimensions. The data set used in this study has been previously analyzed for receptive/expressive dimensionality using revised modified parallel analysis and confirmatory factor analysis ( Tomblin & Zhang, 2006 ). This analysis concluded that the measures used in the study “are not likely to be able to reflect reliable differences within individuals with respect to receptive and expressive modalities” (p. 1206). Hence, despite the use of different tasks in receptive and expressive vocabulary measures in this study, the latent trait measured does not seem to be different. Therefore, in this study, vocabulary skill is operationalized as a composite score, including both receptive and expressive measures.

This study will test the specific prediction that rate of vocabulary growth is related to reading skill by examining the growth in oral vocabulary in an epidemiologically based sample between the fourth and 10th grades among children with a wide range of reading abilities, established at the fourth grade. The first specific question of this study is, is there evidence that fourth-grade word-reading skill is related to the rate of change of vocabulary growth between the fourth and 10th grades after accounting for individual differences in the level of vocabulary acquisition prior to reading instruction? In the current study, vocabulary skill in kindergarten is used as a measure of these individual differences in word learning prior to formal reading instruction. The hypothesis, based on Stanovich's (1986) model, is that that there will be a relationship between fourth-grade reading skill and the rate of vocabulary growth in the years between the fourth and 10th grades.

The second specific question of this study is, if there is a relationship between reading skill and vocabulary growth, is this relationship the same for both strong and weak readers? In other words, if a Matthew effect exists, is it a one-sided or a two-sided Matthew effect? There was no hypothesis for the second question in this study because no previous research has addressed this specific question and there might be some reason to expect either a two-sided or a one-sided Mathew effect. As Shefelbine (1990) pointed out, readers with lower initial vocabulary knowledge will necessarily have an impoverished semantic context for inferring new word meaning, which might lead to lower rates of vocabulary growth. On the other hand, those same readers are less likely to encounter ceiling effects because any given text is more likely to include words that are novel to them. This argument made by Shefelbine (1990) , however, concerns the effect of initial vocabulary skill on vocabulary growth. This is in contrast to the current study, which addresses the relationship of reading skill and vocabulary growth.

The data analyzed in the current study were drawn from a sample of 604 participants who originally took part in an epidemiologic study of language impairment ( Tomblin et al., 1997 ). The original epidemiologic sample participated in the 1993–1994 school year and consisted of 7,218 kindergarten students, representing all available kindergarten students who were monolingual English speakers in selected schools in rural, urban, and suburban areas in Iowa and Illinois. In this initial sample, a stratified cluster sample was used, with stratification by residential setting and cluster sampling according to school ( Tomblin, 2014 ). All students who failed the initial screening were given a diagnostic battery of language and cognitive measures, as were a representative sample of students who passed the screening, such that the group who passed the screening battery and the group who did not were of equal size. Each of these participants was recruited to be part of the longitudinal study, and all who consented became participants in the longitudinal study. All children who completed the longitudinal study and for whom vocabulary scores were available ( n = 485) were included in this sample. The children in fourth grade averaged 10.0 years ( SD = 0.40), and in the 10th grade, they averaged 15.8 years ( SD = 0.37).

The original sample of 485 children contained an oversample of children with poor language abilities. Because this oversampling was applied systematically to a population sample, it was possible to derive a weighting system that adjusted for this; that is, scores were weighted by multiplying each child's score by a constant that was equal to the expected prevalence of that diagnostic category and gender divided by the actual prevalence of those children in the sample. In this manner, children with poor language received proportionally less weight in the analyses than did children who showed typical language, a weighting procedure has been described in other published work involving the sample ( Catts et al., 1999 , 2005 ). The resulting sample of 485 children contained an equal proportion of boys and girls (50% of each). The distribution of the mothers' educational level was as follows: 4% had less than a 4-year high school education, 28% had a high school diploma, 41% had postsecondary education, 15% were college graduates, and 12% had postgraduate education.

As shown in Table 1 , standardized language measures in the fourth grade and performance IQ measured in the second grade before weighting were below the expected population means. However, after weighting the samples, they are very representative of a normal population. The current study uses data from all participants, with weighted measures for all analyses.

Descriptive statistics of sample with regard to oral language measures in the fourth grade and performance IQ obtained in the second grade.

All tasks were administered as part of a larger longitudinal study (for a complete description, see Tomblin & Nippold, 2014 ). Administration of tasks was standardized, and each examiner was given detailed training and monitoring by a data-collection manager, with a minimum of 5% of examination sessions scored blindly by both the examiner and the data collection manager to ensure consistency in scoring, as well as in administration ( Tomblin, 2014 ). Scoring of all tasks was done relative to the child's age at the time of testing.

For the present study, the following vocabulary measures were analyzed: in kindergarten, the Picture Identification and Oral Vocabulary subtests of the Test of Language Development–Primary: Second Edition ( Newcomer & Hammill, 1988 ), and in older grades, the Peabody Picture Vocabulary Test–Revised (PPVT-R; Dunn & Dunn, 1981 ) as well as the Expressive subtest of the Comprehensive Receptive and Expressive Vocabulary Test (CREVT; Wallace & Hammill, 1994 ). Receptive vocabulary measures were picture identification tasks, and expressive vocabulary measures were definition-generation tasks. Each of these is a well-established standardized measure, and information about their validity, specific to this data set, has been published ( Tomblin, Nippold, Fey, & Zhang, 2014 ).

For the present study, the following reading measures were analyzed: the Word Attack (WA) and Word Identification (WI) subtests of the Woodcock Reading Mastery Test–Revised ( Woodcock, 1987 ), which involve reading nonwords (WA) and sight words (WI). These measures are considered to be reliable assessments of word-reading skill ( Cooter, 1989 ).

Composite Developmental Ability Scores

Analysis of growth in a cognitive ability such as vocabulary requires that the children's performance be scaled on a continuum across the developmental period of interest. The principal challenge for the creation of a developmental scale is that the ability of the children must be measured by different items at different developmental time points; thus, the items need to be equated with each other in some meaningful way across development. In this study, developmental ability scores were computed using a Rasch model of item response theory (IRT). The resulting scores are often viewed as being well suited for growth curve modeling ( O'Malley, Francis, Foorman, Fletcher, & Swank, 2002 ). Within IRT, the probability of an item being passed in a test is a function of the participant's ability level, the item's difficulty, as well as its discrimination and the probability of guessing. When calibrating ( Mislevy & Bock, 1998 ), guessing can be set as a constant, and the probability for given items can be calculated from the administration of test items to participants. Thus, item difficulty could be calculated by holding examinee's ability constant. This is termed item calibration ( Mislevy & Bock, 1998 ). Items that were administered across more than one grade level, and which had overall pass rates of between 10% and 90%, were used as anchors ( Vale, 1986 ) for this item calibration. These anchor items were then used to calibrate item difficulty across age levels. For example, if Items 8 and 9 were given to fourth graders, and Items 9 and 10 were given to eighth graders, Items 8 and 10 can be calibrated via their overlap with Item 9 across grades. Table 2 provides a list of the specific items from the PPVT-R and the CREVT at each grade level used in the item calibration, resulting in Rasch-scaled vocabulary ability scores across the fourth to the 10th grades. The difficulty and the discriminating estimates for these items, along with estimates of expressive and receptive vocabulary ability for each examinee at each grade level, were computed using the computer program Bilog ( Mislevy & Bock, 1998 ). Item parameters were determined using marginal maximum likelihood estimation. The 0 value on the scale was set for the average 6-year-old. Resulting ability scores provided a means of measuring the examinees' ability across time.

Items from the Peabody Picture Vocabulary Test–Revised (PPVT-R) and the Comprehensive Receptive Expressive Vocabulary Test (CREVT) used in the item calibration.

Weighted Scores

As described above, weighted scores were used in the analyses of this study to correct for the high rate of language and/or cognitive impairment. This weighted scoring procedure is possible because of the availability of data from the carefully sampled pool of participants in the epidemiological sample. This ensures that the data analyzed in this study are representative of the epidemiological sample, including children with and without a history of language impairment.

Composite Scores

A composite score was derived for vocabulary for each participant, as discussed earlier. The composite was the mean of the developmental ability scores for receptive and expressive vocabulary. These composite scores were used to plot vocabulary growth curves.

In a similar vein, a composite score for word reading was calculated from the WA and WI scores at the fourth grade. A composite of these scores was used to incorporate the earlier developing skill of reading nonwords with the later developing skill of context-free word recognition ( Tunmer & Chapman, 2012 ). Within the context of this study, these skills were used to index basic reading skills in the fourth grade. We expect that these skills are also indirectly indicative of the volume and variety of reading experience that these children will obtain after the fourth grade. This assumption is supported by a meta-analysis by Mol and Bus (2011) that indicated moderate correlations between print exposure and measures of WI and WA during elementary school years. Because the word-reading scores were part of the analysis at a single time point only, developmental scores were not required.

Multilevel modeling . Multilevel modeling was used to test the questions in this study, a method that is expected to yield comparable results to latent growth curve analysis ( Chou, Bentler, & Pentz, 1998 ). Multilevel modeling of the weighted data in this study consisted of fitting each participant's vocabulary ability across the fourth, eighth, and 10th grades with parameters of intercept and linear slope. These parameters served as random effects in combination with a fixed effect of fourth-grade word reading as well as with the covariate of kindergarten vocabulary and their interactions with time (age) in a mixed model analysis using Proc Mixed software ( SAS Institute, 2011 ).

The particular question of interest was whether the slope in vocabulary differed in accord with variation in fourth grade word reading. However, it could be argued that any association between word reading and vocabulary growth in later school years was merely because strong word learners become strong readers. To the extent that this is the case, the basis for the relationship would not be attributable to a special influence of reading on vocabulary. To address this, we also included the kindergarten vocabulary abilities of these children in this analysis as a covariate in this model on both the slope and the intercept. This provides a test of whether word reading is related to vocabulary after controlling for the children's word-learning achievement during the years prior to formal reading instruction. This was considered to be a direct test of the long-term relationship between word-reading skill and vocabulary development.

Because word reading was related to the rate of vocabulary growth, we computed the effect size in the form of f 2 , which reflects the amount of variance in individual differences in vocabulary explained by fourth-grade reading after controlling for kindergarten vocabulary. This measure of the effect of reading on vocabulary growth concerns differences in slopes. A key feature of differential growth rates is that the individual differences accumulate over time; thus, the effect of the predictor variable—in this case, fourth-grade reading—on the outcome variable is likely to increase. Therefore, we measured the degree of association between fourth-grade word-reading ability and 10th-grade vocabulary after controlling for fourth-grade vocabulary ability.

Individual differences in vocabulary growth . Question 2 asked whether the effects of fourth-grade word reading on vocabulary growth were equally distributed across the range of word-reading ability. To do this, growth rates were contrasted between three groups of children categorized according to whether they had high, medium, or low fourth-grade word-reading ability. Vocabulary growth curves were plotted for participants with high word-reading skill (those who scored in the 80th percentile and above), middle word-reading skill (those who scored in the 40th–60th percentile range), and low word-reading skill (those who scored in the 20th percentile and below). We then used mixed modeling to contrast the middle group with the high and low groups with regard to growth rates.

Vocabulary Growth Curves

As expected, the mean of the composite developmental ability scores for vocabulary showed an increase in vocabulary knowledge at each time interval, beginning in kindergarten ( Figure 1 ). The average vocabulary score for the fourth-grade children was 2.26 ( SD = 0.56) and was 3.59 ( SD = 0.61) by the 10th grade. Figure 1 shows that the shape of the growth function was clearly nonlinear, with higher rates of vocabulary growth in early grades. However, between the fourth and 10th grades, the change was much more linear; thus, a linear model of vocabulary growth during this period of development was suitable. Figure 2 shows the mean vocabulary growth curves for readers with low, medium, and high reading skill in grade 4.

Distribution of developmental ability scores for vocabulary at each observational interval from kindergarten through 10th grade for all children in the longitudinal study

Vocabulary scores of participants grouped by word-reading skill (high-level readers, readers in the 80th percentile and above; midlevel readers, readers in the 40th–60th percentile range; low-level readers, readers in the 20th percentile or below).

As anticipated, vocabulary ability upon school entry at kindergarten was correlated with vocabulary ability at fourth ( r = .39, n = 485, p < .0001), eighth ( r = .52 n = 485, p < .0001), and 10th grades ( r = .55, n = 485, p < .0001). These correlations show that vocabulary ability at the onset of reading is associated with subsequent vocabulary ability, and therefore it is likely that there are factors influencing vocabulary growth in children that are not an outgrowth of their reading. In the subsequent analyses, the child's vocabulary ability in kindergarten will be used to represent these non–reading-related vocabulary learning skills and will be used as a covariate in order to better isolate later growth in vocabulary that is associated with reading ability.

Multilevel modeling using Proc Mixed software was used to test for differences in vocabulary growth across time where the child's age at testing was used to reference time. The results of this modeling are shown in Table 3 . These results show that the mean vocabulary intercept (average vocabulary at age 9) before entering covariates (unconditional model) was 2.16 units of developmental ability score, and the mean growth rate was 0.24 points per year. Plots of the modeled values from a random sample of children at different levels of fourth-grade reading levels are shown ( Figure 3 ). Because a linear model was used, these growth functions do not have the nonlinear quality of the data in Figures 1 and ​ and2 2 but, otherwise, these modeled data are similar to the obtained data.

Tests of random (Level 1) and fixed effects for vocabulary growth using kindergarten vocabulary as a covariate.

Note.  KV = kindergarten vocabulary; 4GR = fourth grade reading.

Linear growth functions for random samples of readers for readers in high (80th percentile and above), medium (40th–60th percentile range), and low (20th percentile or below) skill groups for word reading in fourth grade.

Our first question concerned the degree to which fourth-grade reading is associated with vocabulary development (rate of change of vocabulary scores). This was tested within a conditional mixed model as also shown in Table 3 . Table 3 shows that after controlling for their kindergarten vocabulary level, the rate of growth in vocabulary between the fourth and 10th grades was significantly associated with the children's fourth-grade reading level, F (1, 485) = 15.88, p < .0001. The parameter value for this effect was .001, which indicates that higher word reading ability in the fourth grade is associated with greater rates of vocabulary growth, which can be seen in Figure 2 . It should be emphasized that this effect was estimated after the sources of variance concerned with kindergarten reading on overall vocabulary and change in vocabulary between the fourth and 10th grades had been entered into the model. Individual differences in kindergarten vocabulary also had a significant effect on the intercept of vocabulary score at the fourth grade, F (1, 485) = 19.60, p < .0001, and on the growth of vocabulary, F (1, 485) = 6.64, p = .01. Thus, children with higher vocabulary in kindergarten were likely to have higher vocabulary in the fourth grade and show greater rates of vocabulary growth after controlling for the effects of fourth-grade reading ability.

The slope parameter was found to be 0.001. This value represents the rate of change in vocabulary ability scores per year from the fourth through 10th grades that can be attributed to a 1-point change in fourth-grade word-reading skill. The average change in vocabulary ability across the 6 years was 1.38; however, some children changed by as much as 3.62 points and some actually declined by as much as −0.018. The relative size of the effect of fourth-grade reading ability on vocabulary growth compared with the overall growth in vocabulary can be represented using Cohen's f 2 , which reflects the proportion of variance of a single variable within the context of a multivariate regression model. Using a method developed by Selya, Rose, Dierker, Hedeker, and Mermelstein (2012) , we estimated that the individual differences in fourth-grade reading ability accounted for 8% ( f 2 = .08) of the variance in vocabulary growth rates from the fourth to 10th grades. Cohen's f 2 of this size are generally regarded as small to medium in magnitude.

The effect size above reflects the degree to which fourth-grade word reading accounts for variation in the slopes of vocabulary growth across children. Although this effect size is somewhat small, we need to consider that differences in growth are likely to accrue over time. Thus, even small differences in growth rates can lead to substantial long-term effects in absolute vocabulary skill. In order to examine this cumulative effect of differential growth, we computed the magnitude of the effect of fourth-grade word reading at 10th-grade vocabulary after controlling for fourth-grade vocabulary. Thus, this reflects the gain in the effect of reading on vocabulary between the fourth and 10th grades. This resulted in an η 2 partial = .26. Eta squared values of this magnitude are viewed as large, and thus we can see that small to moderate effect size of differential vocabulary growth can result in a large effect given sufficient time.

We can also interpret the magnitude of this effect by comparing this effect size to something that is more familiar. In this case, we can compare the effect of mother's education level on vocabulary development during this same period. It is known that socioeconomic status is related to early vocabulary levels (e.g., Hart & Risley, 1995 ), and we would expect that the effect of maternal education would also extend into the school years. Using the same approach to compute f 2 , we estimated the effect size for maternal education on vocabulary growth between the fourth and 10th grades and found that it was f 2 = .08. Thus, the effect obtained for fourth-grade reading ability on vocabulary is the same as that for maternal education.

The analysis above introduces the question of whether maternal education could be confounded with fourth-grade reading ability and whether this is the reason the effect sizes are similar. In this case, it could be argued that it is the child's home environment that explains the differential growth in vocabulary. However, in our test of reading effects on vocabulary between the fourth and 10th grades, we had controlled for kindergarten vocabulary, and one of the reasons for this was to control for socioeconomic factors that influence vocabulary growth. We tested this assumption by introducing both mother's education and kindergarten vocabulary in the same model and found that mother's education was not a significant predictor, F (4, 474) = 0.24, p = .91, of vocabulary growth after including kindergarten vocabulary. Thus, our inclusion of kindergarten vocabulary did effectively serve as a proxy variable for mothers' education.

The second question asked whether growth rates in vocabulary differed for the three groups of readers. This was addressed by performing a multilevel modeling analysis where the three groups of readers (high, medium, and low) were identified according to their fourth-grade word reading. Figure 2 is a plot of vocabulary growth functions for high-, medium-, and low-skill readers (as defined earlier). A pattern of divergence was shown. The significant effect of fourth-grade reading on growth confirms that differential growth in vocabulary exists in accord with fourth-grade reading; however, this differential could be concentrated in one region of reading ability. Contrasts in the group vocabulary growth between the low-level readers and the midlevel readers showed that the growth slope of the low readers was −0.02 ( SE = 0.0158) lower than that for the midlevel group, which was not significantly different, t (289) = 1.25, p = .21. In contrast, the growth slope of the high-level readers compared with the midlevel readers was 0.04 ( SE = 0.0160) higher, which was significantly different, t (289) = 2.18 , p = .03. These results suggest that the association of fourth-grade reading ability and subsequent vocabulary growth varied somewhat depending on the reading level; that is, the effect was a one-sided, not a two-sided, Matthew effect. In this case, the readers in the upper 20 percentile showed divergence in vocabulary growth relative to those in the middle or low levels of the reading ability distribution.

Relationship of Reading to Vocabulary Growth

The first specific question of this study was whether there was evidence that fourth-grade word-reading skill was related to the rate of change of vocabulary growth between the fourth and 10th grades after accounting for individual differences in vocabulary acquisition prior to reading instruction. Our results strongly support an association between word-reading ability and the rate of subsequent vocabulary growth as measured via an oral language task. It is quite unlikely, however, that word-reading ability in the fourth grade alone is sufficient to explain these results. Instead, we view our measure of fourth-grade reading ability as an indicator variable that is associated with reading-related activities of the children that unfolded between the fourth and 10th grades. These reading-related activities serve as the primary causes of vocabulary growth found in this study. We might add that the type of text the child is reading is also expected to be a variable because reading material that exposes the child to a wider range of vocabulary should also benefit vocabulary growth. Related to this point, Pfost, Dorfler, and Artelt (2013) reported that time reading narratives was much more predictive of vocabulary than was time reading newspapers, magazines, comics, or nonfiction. Thus, the results of this analysis are consistent with Stanovich's (1986) proposal as well as with that of Nagy et al.'s (1985) view that vocabulary growth during school years is largely due to incidental learning from written contexts. Given the importance of reading activity, we would ideally have measured these variables. Within this project, several measures of engagement in reading, such as author recognition, were collected but were found to be of questionable validity. However, other studies have shown an association between reading skill and the volume of reading experience ( Allington, 1983 ; Martin-Chang & Gould, 2008 ; Nagy & Anderson, 1984 ). Nonetheless, although we assume that reading experience is a mediator of the relationship between word reading and the outcome of vocabulary growth, this mediation was not tested as part of this study. Therefore, the results of the current study do not allow us to draw conclusions about whether reading experience is, indeed, the mediator of the effect we found.

Once kindergarten vocabulary levels were accounted for, word reading in the fourth grade accounted for 8% of total variance in rates of vocabulary growth between the fourth and 10th grades. This means that the effect of word reading on vocabulary growth is not trivial. In fact, the size of the effect of word reading on vocabulary growth rates is comparable to the effect of maternal education on vocabulary growth rates during the same developmental period. When the impact of that rate difference is considered in terms of absolute vocabulary levels in the 10th grade, the effect is large.

As Stanovich (2000) stated, his 1986 article contains “many micropredictions and microtheories” (p. 150). Previous studies of other Matthew effects have reported variable results. There might be several reasons for these equivocal results for Matthew effects in previous studies. First, one would not expect to find Matthew effects for all reading-related variables. Paris (2005) defined constrained skills as skills that are limited in scope, are learned quickly, and require the same material to be mastered by all learners, and argued that developmentally constrained skills “should not be conceptualized as enduring individual difference variables” (p. 184). Where outcome variables in other studies were constrained skills, such as word attack skills, one might not expect to find meaningful differences, especially for older or more skilled readers. Reading comprehension, on the other hand, is affected by different component skills through reading development. For very early readers, reading comprehension skill is largely a function of word reading or decoding skill. For more advanced readers, language comprehension skills make a more substantial contribution to reading comprehension. Because the components affecting reading comprehension scores differ in their contribution through development, longitudinal comparisons of reading comprehension skills may or may not show a Matthew effect. These challenges are compounded when combined measures of word reading and reading comprehension are used. Hence, it is possible that some previous studies have not found evidence to support the existence of a Matthew effect because the outcome measures were either developmentally constrained or were developmentally less constrained but were measured in age ranges before the effects would be expected to occur. This analysis would be supported by the meta-analysis of Pfost and colleagues ( Pfost et al., 2014 ), which suggested that there was less evidence for a Matthew effect for developmentally constrained variables such as decoding accuracy. In the case of the current study, reading would be expected to affect vocabulary growth after children are exposed to a large number of novel words through reading, beginning at about the third or fourth grade. This is the developmental point investigated in this study.

Second, as discussed earlier, previous studies of a Matthew effect for vocabulary did not control for word-learning skills prior to formal reading instruction. Indeed, the results of the current study indicate that this variable has a significant effect on the rate of vocabulary growth in the years between the fourth and 10th grades. This may be another reason for the variable findings in previous studies.

Third, the current study used developmental ability scores based on IRT to allow for meaningful comparisons between performance at different age groups, which was not true of previous studies of a Matthew effect for vocabulary. The rationale for the use of IRT-based scores was that they appear to have the best properties, such as an equal-appearing interval scale, for characterizing the growth of mental abilities. Concerns have been raised as to whether these scores are likely to show declining variance with increases in age, whereas grade-equivalence scores seem to produce increasing variance (e.g., Hoover, 1984 ; Yen, 1986 ). These patterns, however, have not been consistently reproduced ( Williams, Pommerich, & Thissen, 1998 ), and it remains unclear whether, or under what circumstances, IRT scores or other forms of developmental scores misrepresent the changes in ability over time. Nonetheless, the use of IRT (Rasch) scores has been critiqued in investigations of a Matthew effect fan spread ( Bast & Reitsma, 1998 ; Stanovich, 2000 ) on the grounds that forcing within-age scores into a normal distribution could cause a decrease in developmental score variance with age ( Hoover, 1984 ). For example, Stanovich (2000) proposed that the use of developmental ability scores could account for the compensatory effect found for reading scores in the study by Shaywitz et al. (1995) . In the current study, the use of developmental ability scores did not result in a decline in variance, and thus did not prohibit our ability to detect an effect of word-reading ability on vocabulary growth. It may be that the results of Shaywitz et al. (1995) with respect to reading are due to the use of a reading composite score that includes word identification, pseudoword identification, and reading comprehension. As Bast and Reitsma (1998) also argued, a composite score with these three skills would not have comparable meaning over time, which significantly obscures the interpretation of the results.

Also, the current study used an epidemiologically based sample. This study was conducted with a group of children who came from a population sample and are therefore more diverse than are often found in research studies, especially where participants need to come into a laboratory setting. Thus, the findings of the present study are more likely to be representative of the population at large.

Relationship Between Reading and Vocabulary Growth Across Reading Skill Levels

The second question of this study was whether the relationship between reading skill and vocabulary growth was the same for both strong and weak readers. Indeed, further examination of the data revealed that the effect of early word-reading ability on vocabulary was not uniform across different levels of initial word-reading ability. Instead, it would appear that the strong readers made greater vocabulary gains relative to the average and weak readers. In the language of the Matthew effect, the rich were getting richer due to their better reading, but the poor were not getting poorer due to their weak reading. Morgan et al. (2008) also reported a Matthew effect that did not apply to both strong and weak readers although they reported asymmetry in the opposite direction, with students most at risk of reading disorders being more likely to fall behind in reading, whereas those least at risk not gaining with respect to typical readers. A different prediction of the Matthew effect model was being tested in this study, and this is likely to account for the difference in results.

In the current study, several factors might account for this one-sided Matthew effect, with a nonuniform effect of word-reading skill on vocabulary growth across skill levels. The current study does not differentiate between these possibilities and, naturally, they are not mutually exclusive. The first possibility is that the gap in reading volume between strong and average readers is greater than the gap in reading volume between average and weak readers. The possibility that there are larger differences in reading volume between strong and average readers, compared to the differences between average and weak readers, is somewhat speculative. However, Cunningham (2005) discussed data indicating that, for independent reading in fifth-grade students, the absolute differences between avid and average readers (90th and 50th percentiles for reading volume) are greater than are the absolute differences between average and weak readers (50th and 10th percentile for reading volume). This would be consistent with the hypothesis that differences in reading experiences are not in a linear relationship with skill level. In addition to the amount of reading in which individual children engage, it may also be that the reading material selected by strong readers contains a greater degree of novel vocabulary than does the material assigned to, or selected by, average or weak readers.

The second possibility is that students differ in the amount that they benefit from reading new words and that those individual differences are greatest between average and strong readers. There is evidence that children differ in their ability to derive word meanings from written contexts ( Cain, Oakhill, & Elbro, 2003 ; Cain, Oakhill, & Lemmon, 2004 ; McKeown, 1985 ). Individual differences in word learning through text are related to differences in working memory and to the ability to learn new vocabulary in a direct instruction task ( Cain et al., 2004 ). The existence of these individual differences motivates interventions to improve children's skill in deriving word meanings from context ( Cain, 2007 ; Goerss, Beck, & McKeown, 1999 ; Nash & Snowling, 2006 ). Genetic evidence also provides support for the idea that environmental factors may have a nonuniform effect on vocabulary growth across skill levels. DeThorne, Petrill, Hayiou-Thomas, and Plomin (2005) reported that children with very low vocabulary scores had a higher heritability and a lower influence of shared environment, relative to children with less severe vocabulary deficits. Again, the possibility that these individual differences are greater between strong and average readers, compared to the differences between weak and average readers, is speculative.

It is also possible that weak readers were provided with educational interventions for reading skill or vocabulary knowledge, which reduced the cumulative disadvantage effect for them. The current study does not include information about intervention history, so this is speculative. However, this data suggests that the combination of behaviors chosen by students, differences in the ability to learn from exposure to new words in text, and educational policies are not further disadvantaging weak readers, at least in terms of their vocabulary growth. For those who are concerned about the poor getting poorer, this is an encouraging finding.

Limitations of the Current Study

As with any nonexperimental design, these conclusions are based on associations rather than on stronger experimental evidence involving random assignment to independent variable treatment conditions. The limitation of an observational design, such as this study, is that other confounding variables may play a role in observed effects. One such confounder could be the initial vocabulary level. Children with better word-reading skills in the fourth grade are also likely to have better listening vocabulary, as was true of the participants in this study. As a result, our analysis incorporated a measure of kindergarten-listening vocabulary ability as a covariate. This analysis is possible, in part, because the current study analyzes data from a large longitudinal sample of 485 participants, which provides adequate statistical power. We can assume that this measure of vocabulary in kindergarten was largely unaffected by the child's reading experience, but would reflect the child's general vocabulary–learning ability along with aspects of the child's environment that could be associated with individual differences in word learning. Thus, we can argue that the effects of fourth-grade reading ability on subsequent listening vocabulary are likely to be independent of the child's general vocabulary–learning ability.

Another limitation of the current study is the use of a linear model, which means that the vocabulary data between the fourth and 10th grades was fit using only a linear slope and intercept. However, the overall vocabulary scores between kindergarten and 10th grade suggest a curvilinear trend, and it is possible that the data might be better represented by a nonlinear model, but this would require data at more time points than is available with this data set. This means that the current analysis cannot address questions that are specific to acceleration or deceleration of vocabulary growth rate, but instead captures the primary feature of the growth trajectory, namely overall change through time.

The purpose of the original longitudinal study was to answer questions regarding outcomes of children with language impairment. The oversampling of children with language and/or cognitive impairments could potentially have biased the results of the current study. However, this was accounted for with the use of weighted scores. The findings, therefore, apply to both children and adolescents who were language impaired and typically developing.

The principal finding of this study is that fourth-grade reading-word skill was related to the rate of change in vocabulary growth between the fourth and 10th grades, controlling for preliterate vocabulary skill. We interpret measures of word reading in the fourth grade as being an indicator variable for a variety of reading-related activities occurring during and after the fourth grade, which would affect exposure to new words. The analysis controlled for vocabulary levels prior to formal reading instruction and used developmental scores based on IRT, addressing two potential limitations in studies of Matthew effects. Data in the current study was collected from a population-based sample, meaning that these findings apply to both readers who are typically developing and language impaired. Hence, the current study provides strong support for the existence of a Matthew effect between word-reading skill and vocabulary. It is significant that the magnitude of the effect on absolute vocabulary levels was found to be large. The effect seems to be driven by strong readers, rather than weak readers, an encouraging finding for those concerned about outcomes for weak readers. More broadly, these findings point to the importance of reading to the process of vocabulary acquisition in older children and adolescents.

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100+ Research Vocabulary Words & Phrases

The academic community can be conservative when it comes to enforcing academic writing style , but your writing shouldn’t be so boring that people lose interest midway through the first paragraph! Given that competition is at an all-time high for academics looking to publish their papers, we know you must be anxious about what you can do to improve your publishing odds.

To be sure, your research must be sound, your paper must be structured logically, and the different manuscript sections must contain the appropriate information. But your research must also be clearly explained. Clarity obviously depends on the correct use of English, and there are many common mistakes that you should watch out for, for example when it comes to articles , prepositions , word choice , and even punctuation . But even if you are on top of your grammar and sentence structure, you can still make your writing more compelling (or more boring) by using powerful verbs and phrases (vs the same weaker ones over and over). So, how do you go about achieving the latter?

Below are a few ways to breathe life into your writing.

1. Analyze Vocabulary Using Word Clouds

Have you heard of “Wordles”? A Wordle is a visual representation of words, with the size of each word being proportional to the number of times it appears in the text it is based on. The original company website seems to have gone out of business, but there are a number of free word cloud generation sites that allow you to copy and paste your draft manuscript into a text box to quickly discover how repetitive your writing is and which verbs you might want to replace to improve your manuscript.

Seeing a visual word cloud of your work might also help you assess the key themes and points readers will glean from your paper. If the Wordle result displays words you hadn’t intended to emphasize, then that’s a sign you should revise your paper to make sure readers will focus on the right information.

As an example, below is a Wordle of our article entitled, “ How to Choose the Best title for Your Journal Manuscript .” You can see how frequently certain terms appear in that post, based on the font size of the text. The keywords, “titles,” “journal,” “research,” and “papers,” were all the intended focus of our blog post.

2. Study Language Patterns of Similarly Published Works

Study the language pattern found in the most downloaded and cited articles published by your target journal. Understanding the journal’s editorial preferences will help you write in a style that appeals to the publication’s readership.

Another way to analyze the language of a target journal’s papers is to use Wordle (see above). If you copy and paste the text of an article related to your research topic into the applet, you can discover the common phrases and terms the paper’s authors used.

For example, if you were writing a paper on  links between smoking and cancer , you might look for a recent review on the topic, preferably published by your target journal. Copy and paste the text into Wordle and examine the key phrases to see if you’ve included similar wording in your own draft. The Wordle result might look like the following, based on the example linked above.

If you are not sure yet where to publish and just want some generally good examples of descriptive verbs, analytical verbs, and reporting verbs that are commonly used in academic writing, then have a look at this list of useful phrases for research papers .

3. Use More Active and Precise Verbs

Have you heard of synonyms? Of course you have. But have you looked beyond single-word replacements and rephrased entire clauses with stronger, more vivid ones? You’ll find this task is easier to do if you use the active voice more often than the passive voice . Even if you keep your original sentence structure, you can eliminate weak verbs like “be” from your draft and choose more vivid and precise action verbs. As always, however, be careful about using only a thesaurus to identify synonyms. Make sure the substitutes fit the context in which you need a more interesting or “perfect” word. Online dictionaries such as the Merriam-Webster and the Cambridge Dictionary are good sources to check entire phrases in context in case you are unsure whether a synonym is a good match for a word you want to replace. 

To help you build a strong arsenal of commonly used phrases in academic papers, we’ve compiled a list of synonyms you might want to consider when drafting or editing your research paper . While we do not suggest that the phrases in the “Original Word/Phrase” column should be completely avoided, we do recommend interspersing these with the more dynamic terms found under “Recommended Substitutes.”

A. Describing the scope of a current project or prior research

B. outlining a topic’s background, c. describing the analytical elements of a paper, d. discussing results, e. discussing methods, f. explaining the impact of new research, wordvice writing resources.

For additional information on how to tighten your sentences (e.g., eliminate wordiness and use active voice to greater effect), you can try Wordvice’s FREE APA Citation Generator and learn more about how to proofread and edit your paper to ensure your work is free of errors.

Before submitting your manuscript to academic journals, be sure to use our free AI proofreader to catch errors in grammar, spelling, and mechanics. And use our English editing services from Wordvice, including academic editing services , cover letter editing , manuscript editing , and research paper editing services to make sure your work is up to a high academic level.

We also have a collection of other useful articles for you, for example on how to strengthen your writing style , how to avoid fillers to write more powerful sentences , and how to eliminate prepositions and avoid nominalizations . Additionally, get advice on all the other important aspects of writing a research paper on our academic resources pages .

50 Useful Academic Words & Phrases for Research

Like all good writing, writing an academic paper takes a certain level of skill to express your ideas and arguments in a way that is natural and that meets a level of academic sophistication. The terms, expressions, and phrases you use in your research paper must be of an appropriate level to be submitted to academic journals.

Therefore, authors need to know which verbs , nouns , and phrases to apply to create a paper that is not only easy to understand, but which conveys an understanding of academic conventions. Using the correct terminology and usage shows journal editors and fellow researchers that you are a competent writer and thinker, while using non-academic language might make them question your writing ability, as well as your critical reasoning skills.

What are academic words and phrases?

One way to understand what constitutes good academic writing is to read a lot of published research to find patterns of usage in different contexts. However, it may take an author countless hours of reading and might not be the most helpful advice when faced with an upcoming deadline on a manuscript draft.

Briefly, “academic” language includes terms, phrases, expressions, transitions, and sometimes symbols and abbreviations that help the pieces of an academic text fit together. When writing an academic text–whether it is a book report, annotated bibliography, research paper, research poster, lab report, research proposal, thesis, or manuscript for publication–authors must follow academic writing conventions. You can often find handy academic writing tips and guidelines by consulting the style manual of the text you are writing (i.e., APA Style , MLA Style , or Chicago Style ).

However, sometimes it can be helpful to have a list of academic words and expressions like the ones in this article to use as a “cheat sheet” for substituting the better term in a given context.

How to Choose the Best Academic Terms

You can think of writing “academically” as writing in a way that conveys one’s meaning effectively but concisely. For instance, while the term “take a look at” is a perfectly fine way to express an action in everyday English, a term like “analyze” would certainly be more suitable in most academic contexts. It takes up fewer words on the page and is used much more often in published academic papers.

You can use one handy guideline when choosing the most academic term: When faced with a choice between two different terms, use the Latinate version of the term. Here is a brief list of common verbs versus their academic counterparts:

Although this can be a useful tip to help academic authors, it can be difficult to memorize dozens of Latinate verbs. Using an AI paraphrasing tool or proofreading tool can help you instantly find more appropriate academic terms, so consider using such revision tools while you draft to improve your writing.

Top 50 Words and Phrases for Different Sections in a Research Paper

The “Latinate verb rule” is just one tool in your arsenal of academic writing, and there are many more out there. But to make the process of finding academic language a bit easier for you, we have compiled a list of 50 vital academic words and phrases, divided into specific categories and use cases, each with an explanation and contextual example.

Best Words and Phrases to use in an Introduction section

1. historically.

An adverb used to indicate a time perspective, especially when describing the background of a given topic.

2. In recent years

A temporal marker emphasizing recent developments, often used at the very beginning of your Introduction section.

3. It is widely acknowledged that

A “form phrase” indicating a broad consensus among researchers and/or the general public. Often used in the literature review section to build upon a foundation of established scientific knowledge.

4. There has been growing interest in

Highlights increasing attention to a topic and tells the reader why your study might be important to this field of research.

5. Preliminary observations indicate

Shares early insights or findings while hedging on making any definitive conclusions. Modal verbs like may , might , and could are often used with this expression.

6. This study aims to

Describes the goal of the research and is a form phrase very often used in the research objective or even the hypothesis of a research paper .

7. Despite its significance

Highlights the importance of a matter that might be overlooked. It is also frequently used in the rationale of the study section to show how your study’s aim and scope build on previous studies.

8. While numerous studies have focused on

Indicates the existing body of work on a topic while pointing to the shortcomings of certain aspects of that research. Helps focus the reader on the question, “What is missing from our knowledge of this topic?” This is often used alongside the statement of the problem in research papers.

9. The purpose of this research is

A form phrase that directly states the aim of the study.

10. The question arises (about/whether)

Poses a query or research problem statement for the reader to acknowledge.

Best Words and Phrases for Clarifying Information

11. in other words.

Introduces a synopsis or the rephrasing of a statement for clarity. This is often used in the Discussion section statement to explain the implications of the study .

12. That is to say

Provides clarification, similar to “in other words.”

13. To put it simply

Simplifies a complex idea, often for a more general readership.

14. To clarify

Specifically indicates to the reader a direct elaboration of a previous point.

15. More specifically

Narrows down a general statement from a broader one. Often used in the Discussion section to clarify the meaning of a specific result.

16. To elaborate

Expands on a point made previously.

17. In detail

Indicates a deeper dive into information.

Points out specifics. Similar meaning to “specifically” or “especially.”

19. This means that

Explains implications and/or interprets the meaning of the Results section .

20. Moreover

Expands a prior point to a broader one that shows the greater context or wider argument.

Best Words and Phrases for Giving Examples

21. for instance.

Provides a specific case that fits into the point being made.

22. As an illustration

Demonstrates a point in full or in part.

23. To illustrate

Shows a clear picture of the point being made.

24. For example

Presents a particular instance. Same meaning as “for instance.”

25. Such as

Lists specifics that comprise a broader category or assertion being made.

26. Including

Offers examples as part of a larger list.

27. Notably

Adverb highlighting an important example. Similar meaning to “especially.”

28. Especially

Adverb that emphasizes a significant instance.

29. In particular

Draws attention to a specific point.

30. To name a few

Indicates examples than previously mentioned are about to be named.

Best Words and Phrases for Comparing and Contrasting

31. however.

Introduces a contrasting idea.

32. On the other hand

Highlights an alternative view or fact.

33. Conversely

Indicates an opposing or reversed idea to the one just mentioned.

34. Similarly

Shows likeness or parallels between two ideas, objects, or situations.

35. Likewise

Indicates agreement with a previous point.

36. In contrast

Draws a distinction between two points.

37. Nevertheless

Introduces a contrasting point, despite what has been said.

38. Whereas

Compares two distinct entities or ideas.

Indicates a contrast between two points.

Signals an unexpected contrast.

Best Words and Phrases to use in a Conclusion section

41. in conclusion.

Signifies the beginning of the closing argument.

42. To sum up

Offers a brief summary.

43. In summary

Signals a concise recap.

44. Ultimately

Reflects the final or main point.

45. Overall

Gives a general concluding statement.

Indicates a resulting conclusion.

Demonstrates a logical conclusion.

48. Therefore

Connects a cause and its effect.

49. It can be concluded that

Clearly states a conclusion derived from the data.

50. Taking everything into consideration

Reflects on all the discussed points before concluding.

Edit Your Research Terms and Phrases Before Submission

Using these phrases in the proper places in your research papers can enhance the clarity, flow, and persuasiveness of your writing, especially in the Introduction section and Discussion section, which together make up the majority of your paper’s text in most academic domains.

However, it's vital to ensure each phrase is contextually appropriate to avoid redundancy or misinterpretation. As mentioned at the top of this article, the best way to do this is to 1) use an AI text editor , free AI paraphrasing tool or AI proofreading tool while you draft to enhance your writing, and 2) consult a professional proofreading service like Wordvice, which has human editors well versed in the terminology and conventions of the specific subject area of your academic documents.

For more detailed information on using AI tools to write a research paper and the best AI tools for research , check out the Wordvice AI Blog .

Training videos   |   Faqs

Useful Phrases and Sentences for Academic & Research Paper Writing

Overview |   Abstract   | Introduction | Literature Review | Materials & Methods | Results & Discussion | Conclusion & Future Work | Acknowledgements & Appendix

1. Abstract

An abstract is a self-contained and short synopsis that describes a larger work. The abstract is the only part of the paper that is published online and in most conference proceedings. Hence abstract constitutes a very important section of your paper.  Also, when you submit your paper to a journal, potential reviewers only see the abstract when invited by an editor to review a manuscript. The abstract should include one or two lines briefly describing the topic, scope, purpose, results, and conclusion of your work. The abstract is indexed by search engines, so make sure that it has all the right words that a fellow researcher in the same field will be using while searching for articles online. Also, make sure it is rich with data and numbers to demonstrate the scientific rigor of your article. Be very clear and confident about your findings. Keep it punchy and straight to the point.

The abstract section of your research paper should include the following:

Click here for the academic phrases and vocabulary for the abstract section of the research paper…

2. Introduction

Introduction section comes after the abstract. Introduction section should provide the reader with a brief overview of your topic and the reasons for conducting research. The introduction is a perfect place to set the scene and make a good first impression. Regarding word count, introduction typically occupies 10-15% of your paper, for example, if the total word count of your paper is 3000, then you should aim for an introduction of around 600 words. It is often recommended that the introduction section of the paper is written after finishing the other sections of the paper. This is because it is difficult to figure out what exactly to put in the introduction section of the paper until you have seen the big picture. Sound very confident about your chosen subject area and back up your arguments with appropriate references. After reading the introduction, the reader must have a clear idea of what to expect from the rest of your research paper.

The introduction section of your research paper should include the following:

• General introduction
• Problem definition
• Gaps in the literature
• Problems solution
• Study motivation
• Aims & objectives
• Significance and advantages of your work

Click here for the academic phrases and vocabulary for the introduction section of the research paper…

3. Literature review

The literature review should clearly demonstrate that the author has a good knowledge of the research area. Literature review typically occupies one or two passages in the introduction section. A well-written literature review should provide a critical appraisal of previous studies related to the current research area rather than a simple summary of prior works. The author shouldn’t shy away from pointing out the shortcomings of previous works. However, criticising other’s work without any basis can weaken your paper. This is a perfect place to coin your research question and justify the need for such a study. It is also worth pointing out towards the end of the review that your study is unique and there is no direct literature addressing this issue. Add a few sentences about the significance of your research and how this will add value to the body of knowledge.

The literature review section of your research paper should include the following:

• Previous literature
• Limitations of previous research
• Research questions
• Research to be explored

Click here for the academic phrases and vocabulary for the literature review section of the research paper…

4. Materials and Methods

The methods section that follows the introduction section should provide a clear description of the experimental procedure, and the reasons behind the choice of specific experimental methods. The methods section should be elaborate enough so that the readers can repeat the experimental procedure and reproduce the results. The scientific rigor of the paper is judged by your materials and methods section, so make sure you elaborate on all the fine details of your experiment. Explain the procedures step-by-step by splitting the main section into multiple sub-sections. Order procedures chronologically with subheadings. Use past tense to describe what you did since you are reporting on a completed experiment. The methods section should describe how the research question was answered and explain how the results were analyzed. Clearly explain various statistical methods used for significance testing and the reasons behind the choice.

The methods section of your research paper should include the following:

• Experimental setup
• Data collection
• Data analysis
• Statistical testing
• Assumptions
• Remit of the experiment

Click here for the academic phrases and vocabulary for the methods section of the research paper…

5. Results and Discussion

The results and discussion sections are one of the challenging sections to write. It is important to plan this section carefully as it may contain a large amount of scientific data that needs to be presented in a clear and concise fashion. The purpose of a Results section is to present the key results of your research. Results and discussions can either be combined into one section or organized as separate sections depending on the requirements of the journal to which you are submitting your research paper. Use subsections and subheadings to improve readability and clarity. Number all tables and figures with descriptive titles. Present your results as figures and tables and point the reader to relevant items while discussing the results. This section should highlight significant or interesting findings along with P values for statistical tests. Be sure to include negative results and highlight potential limitations of the paper. You will be criticized by the reviewers if you don’t discuss the shortcomings of your research. This often makes up for a great discussion section, so do not be afraid to highlight them.

The results and discussion section of your research paper should include the following:

• Comparison with prior studies
• Limitations of your work
• Casual arguments
• Speculations
• Deductive arguments

Click here for the academic phrases and vocabulary for the results and discussion section of the research paper…

6. Conclusion and Future Work

A research paper should end with a well-constructed conclusion. The conclusion is somewhat similar to the introduction. You restate your aims and objectives and summarize your main findings and evidence for the reader. You can usually do this in one paragraph with three main key points, and one strong take-home message. You should not present any new arguments in your conclusion. You can raise some open questions and set the scene for the next study. This is a good place to register your thoughts about possible future work. Try to explain to your readers what more could be done? What do you think are the next steps to take? What other questions warrant further investigation? Remember, the conclusion is the last part of the essay that your reader will see, so spend some time writing the conclusion so that you can end on a high note.

The conclusion section of your research paper should include the following:

• Overall summary
• Further research

Click here for the academic phrases and vocabulary for the conclusions and future work sections of the research paper…

7. Acknowledgements and Appendix

There is no standard way to write acknowledgements. This section allows you to thank all the people who helped you with the project. You can take either formal or informal tone; you won’t be penalized.  You can place supplementary materials in the appendix and refer to them in the main text. There is no limit on what you can place in the appendix section. This can include figures, tables, costs, budget, maps, etc. Anything that is essential for the paper but might potentially interrupt the flow of the paper goes in the appendix.

Click here for the academic phrases and vocabulary for the acknowledgements and appendix sections of the research paper…

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Thanks for your effort. could I have a PDF having all the info included here.

You can control + p and save as pdf

• Pingback: Scholarly Paraphrasing Tool and Essay Rewriter for Rewording Academic Papers - Ref-N-Write: Scientific Research Paper Writing Software Tool - Improve Academic English Writing Skills

thank you so much

if you can also add on verbs used for each section would be good further

First of all, Thanks! I really appreciate the time and effort you put into http://www.intoref-n-write.com/trial/how-to-write-a-research-paper-academic-phrasebank-vocabulary/ ) which have greatly enhanced understanding of “how-to-write-a-research-paper”.

Thank you very much for this 🙂

Thank you very much!

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• Lawrence W. Tyree Library
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*Research 101

Library and research vocabulary.

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As you use the library or research for an assignment, you will encounter specialized terms and words. This list includes many of the more common words you will find, along with definitions. You may also wish to view the  Multilingual Glossary Language Table to translate library terms to seven languages.

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