The importance of vocabulary in social interaction, understanding oral information, and reading is profound (Hjetland et al., 2017), and several meta-analyses have summarized how intervention programs can increase children’s vocabulary development (Elleman et al., 2009; Marulis & Neuman, 2010, 2013; Mol et al., 2009; Rogde et al., 2019; Swanson et al., 2011). These meta-analyses have used different methodological approaches. In particular, the reviews vary in whether they merge researcher-developed treatment-inherent measures (typically vocabulary or listening comprehension tests with trained words embedded) and standardized measures. Despite their variation, all these meta-analyses show to some extent that oral language interventions are effective, yet little is known what actually improves and whether there is a relation between the size of improvement on the trained words and on the standardized measures.
In this paper, we present a meta-analysis that examines the extent to which the two measure types provide different mean effect sizes and whether there is a relation between the gains in researched inherent measures and in standardized tests. We also investigate the moderators of the mean effect sizes and the relation between these two measure types. The meta-analysis that we present in this paper is based on a subset of studies in a previous published Campbell systematic review (Rogde et al., 2019) and corresponding protocol (Rogde et al., 2016).
Studies of oral language interventions typically include researcher-created tests of trained vocabulary and/or standardized measures of vocabulary knowledge. These two outcome types are inherently different. Trained vocabulary outcomes are based directly on the treatment because the test measures knowledge of words that are trained in the instructional program (Slavin & Madden, 2011). In contrast, standardized outcomes are typically standardized tests that are not created for the specific intervention. Slavin and Madden (2011) refer to this type of outcome as a treatment-independent measure. These outcomes are important when researchers and practitioners want to know if learning transfers to a child’s general vocabulary knowledge. Importantly, the two outcome types differ regarding the effect sizes that can be expected from vocabulary intervention. Since trained vocabulary measures test the understanding of instructed words to which only the treatment group has been exposed, the effects are obviously expected to be positive when compared to those in a control group (see Slavin & Madden, 2011 for a discussion). Conversely, the expectations of gaining effects on standardized tests are based on the theory that some components of an intervention program will lead to transfer effects on these vocabulary outcomes.
Reviews in the educational field present a confusing picture as they treat these types of outcomes differently in their syntheses and analyses. Some reviews have excluded treatment-inherent measures (e.g. Rogde et al., 2019; Slavin et al., 2011), others have included both types of measures yet made separate analyses of the outcomes (e.g. Elleman et al., 2009), and several have synthesized a mean treatment effect based on both types of outcomes (e.g. Swanson et al., 2011). Slavin and Madden (2011) find that the What Works Clearinghouse (2008a, 2008b) reading and math reviews averaged effect sizes from measures that clearly produced different estimates. Several examples of published meta-analyses on vocabulary intervention programs have also averaged the effect sizes from both trained vocabulary and standardized vocabulary outcomes in their analyses. For instance, Marulis and Neuman (2010, 2013) report an overall effect size of d = 0.88 and d = 0.87, respectively, nearly one standard deviation (SD) on vocabulary measures in both studies. Mol et al. (2009) show an overall effect size of d = 0.62 for expressive vocabulary and d = 0.45 for receptive vocabulary, and Swanson et al. (2011) report d = 1.02 for vocabulary outcomes based on both trained and standardized tests. A recent review by Rogde et al. (2019) averages the effect sizes from 43 trials examining the effects of vocabulary instruction in educational settings. The overall effect size for vocabulary outcomes is solely based on standardized vocabulary measures, displaying an overall modest effect size of g = 0.13. Although these reviews are quite different in their inclusion criteria for eligible studies, the large differences in the synthesized overall effects are likely (at least partly) explained by their varied approaches to including trained vocabulary outcomes in their analyses of effects. In addition, previous meta-analyses of vocabulary instruction have included studies without an appropriate control group. In contrast, the current review solely include randomized controlled trials (RCTs) and quasi-experiments (QEs) with control groups and measures of baseline differences. Thus, prior reviews in the field have thus far not clearly contrasted the differing effects of these outcome measures.
Relation between treatment-inherent and standardized outcomes
While it is known that interventions targeting oral language can be effective, little is known about what drives these effects. An important question is whether gains in transfer measures (i.e. standardized tests) are related to the gains observed in specific words that are trained in the intervention. There are several theoretical reasons for expecting a relation between gains in the trained words and gains in the standardized measures. One reason why learning trained words can relate to the effects on the standardized measures is provided by the primitive elements theory (Taatgen, 2013). This suggests that transfer can happen because the intervention may improve children’s ability to explain not only the specific words in which they are explicitly taught but also words in general. Thus, transfer occurs when the set of procedures learned with the trained words can also be utilized for untrained words (Taatgen, 2013).
Another theoretical reason for why transfer between trained and untrained words can occur is based on the vector semantics theory (Jurafsky & Martin, 2014). In line with this, transfer might occur because learning new words provides children with an improved understanding of the words that they already know. In line with the predictions from vector semantics, if a new word is similar either in syntax or semantics to a word already known by the child, this increases the probability that the child will learn the new word (Jurafsky & Martin, 2014). For example, if the child knows the word damp, it will be easier to learn the word moist since the two are semantically related. Similarly, learning the word moist may also offer the child a more nuanced understanding of damp.
Importantly, the opposite could also be the case, that is, it can also be deduced from the theory of broader transfer that no relation exists between the effects on trained words and on general language tests. In line with the theories of Bransford and Schwartz (1999) and Detterman (1993), it is not possible to detect transfer by training in one skill and testing whether it is directly applicable to another skill. This way of evaluating transfer is too restrictive because transfer occurs on a more general level and affects broader skills, such as critical thinking and meta-cognition (Bransford & Schwartz, 1999; Detterman, 1993; Lee, 1998).
A recent study that has examined the relation between trained words and standardized measures has found a relation between them on expressive language measures but not on receptive ones (Melby-Lervåg et al., 2020). The finding that the effects of training and the transfer effects are solely related to expressive measures could indicate that the primitive elements theory explains this transfer (Taatgen, 2013). Thus, one aspect that seems to improve and transfer to the untrained words is children’s ability to develop procedures to provide better explanations of words. The primitive elements theory predicts that transfer is possible between tasks that share the same basic underlying structure and similar operators or procedures.
The current study
Our current study has two main aims. Our first objective is to examine what size of difference exists between gains in trained vocabulary and standardized vocabulary outcomes of oral language interventions. The hypothesis is that there would be a large difference in effects between trained vocabulary and standardized vocabulary outcomes. We also aim to perform a moderator analysis of the size of this difference in effects and to examine whether the duration of the instruction would relate differently to the size of the outcome effects of the two different outcomes. It could be that intervention programs of short duration would be associated with larger effect sizes than programs with longer duration for the trained vocabulary outcomes, while an opposite pattern would probably be the case for standardized outcomes. As for the trained words, the closer in time the instruction and testing occur, the more likely children are to remember the meanings of these words. In the longer time frame for instruction, more words are probably trained, and the test is likely to be based on a random selection of words for the entire period of instruction. In contrast, for the effect of standardized vocabulary outcomes, the longer duration of the intervention is assumed to be associated with higher effect sizes. The hypothesis is therefore that moderators related to the duration of the instruction would be differentially associated with the effects on trained vocabulary and general vocabulary outcomes.
Second, we aim to examine the relation between gains in trained vocabulary outcomes and in standardized outcomes. As earlier noted, large effects can be expected on trained vocabulary outcomes that relate to the direct instruction on word meanings in the programs. In contrast, gaining effects on standardized outcomes also depends on whether the instruction has succeeded in providing children with knowledge that has enhanced their disposition to learn new words. If this is based on the transfer of knowledge, we could expect the studies with large gains in trained words to demonstrate the largest gains in the standardized measures. This would be in line with a recent study’s results showing that effects on standardized measures are mediated by effects on trained words (Melby-Lervåg et al., 2020). We also aim to conduct a moderator analysis concerning this relationship, that is, whether the relation between gains in trained words and in standardized measures would be stronger in studies using expressive rather than receptive outcome measures. Because the transfer effects in the study by Melby-Lervåg et al. (2020) have been generated through expressive (not receptive) measures, we would expect to find a stronger relation between trained words and expressive standardized measures.
Our review aims to answer the following questions
1) What difference exists between gains in trained vocabulary (treatment-inherent tests) and in standardized vocabulary outcomes (treatment-independent tests) in oral language interventions?
2) Does the amount of the instruction relate differently to the effects of trained vocabulary tests and standardized vocabulary tests? Does longer treatment contribute to standardized outcomes but not trained vocabulary outcomes?
3) Is there a relation between gains in trained words and gains in standardized measures?
4) If so, is this relation stronger for expressive standardized tests than for receptive ones?
Data collection: Search strategy and screening
The included studies for this review were based on a two-step process, as follows: 1) The first step refers to the strategy used in the paper by Rogde et al. (2019). In Rogde et al. (2019), a comprehensive search was conducted to assess RCTs and QEs conducted in educational contexts with the goal of improving children’s language skills. This study synthesized the effect of language instruction on solely standardized language outcomes. The included studies in the current paper are based on the same search strings and terms that can be found in Appendix 1. 2) The second step of data collection refers to the inclusion of studies for the current meta-analysis reported in this paper. This involved screening for papers in Rogde et al. (2019) for further analyses. At this step, included studies had to report both vocabulary outcomes measured by standardized tests and trained vocabulary outcomes. Thus, the sample of studies in the current review is a subsample of the studies included in the meta-analysis by Rogde et al. (2019). In Rogde et al. (2019), trained vocabulary outcomes reported from the studies were not analyzed.
Criteria for considering studies for this review
RCTs and QEs with a pre–post controlled designs were eligible for inclusion. In addition, QEs with non-random assignment provided evidence that there were no baseline differences judged to be of substantial importance. Still, QEs represent weaker designs with more threats to the validity of causal inferences than RCTs. Imbalances between groups on variables not measured could still exist. The decision to still include QEs was made to be sure we would end up with a sufficient number of studies. The intervention programs had to be conducted in preschool or school up to the end of secondary school. Intervention programs implemented by parents or other persons in the children’s home environment were excluded. The sample of participants could include typically achieving children, second-language learners, children with language weaknesses, or children from low socioeconomic backgrounds. The samples of children with special diagnoses, such as autism and other mental or sensory disabilities, were ineligible for inclusion. To be included in the current review, the studies had to report outcomes of trained vocabulary and standardized vocabulary measured at the same time point. Distinguishing between trained vocabulary and standardized vocabulary would raise the question about whether items in standardized tests could include words trained in an intervention. If this was reported in a trial, the study was excluded. Thus, for studies to be included in the review, an intervention effect on both the following outcome variables had to be reported: trained vocabulary outcomes (researcher-created tests designed to examine the knowledge of directly trained words) and standardized vocabulary outcomes (tests that excluded items explicitly trained in an instructional program).
Studies were excluded for the following three main reasons: The study did not report any outcome of trained vocabulary. The study only reported outcomes that included a mix of target words and untrained words. The study only reported effect sizes of trained vocabulary outcomes as ‘unit tests’ with different assessment time points than those of the standardized vocabulary outcomes.
Measures of treatment effect and training duration. The first and second authors coded the information of interest from all the studies. This included effect sizes for taught vocabulary, effect sizes for standardized vocabulary and training duration. Questions related to the coding of information were discussed within the research team. Details of outcomes and effect sizes for each study are provided in Appendix 2.
Risk of bias assessment. Risk of bias was assessed for each study, coded independently by two of the authors and decided by consensus. The studies were judged as high risk, unclear risk or low risk according to the following four type of biases: selection bias, performance bias, detection bias, attrition bias and reporting bias. This classification is recommended by Higgins et al. (2011). Details of risk of bias assessment and judgement are provided in Appendix 3. For more information about the type of biases and a broader explanation of what the judgements were based on, see Rogde et al. (2019).
Publication bias. Publication bias occurs when a mean effect size is upwardly biased because only studies with large or significant effects are published (i.e. the file-drawer problem with entire studies) or because authors only report data on variables that show effects (often referred to as p-hacking, or the file-drawer problem for parts of studies; see Simmons et al., 2011, 2014). The studies in the current meta-analysis were included in a p-curve analysis for standardized outcomes in the systematic review by Rogde et al. (2019). Information about the p-curve analysis, transparency of data extraction and presentation of the result can be found in Rogde et al. (2019). No evidence of publication bias was detected.
The data were entered into the comprehensive meta-analysis program by Borenstein et al. (2014). The effect sizes were calculated by dividing the differences in gains between pretests and posttests in the treatment group and the control group by the pooled SD for each group in the pretest, a method recommended by Morris (2008). When the effect size was positive, the group receiving vocabulary instruction made greater pretest-posttest gains than the control group. We adjusted the effect sizes for small samples using Hedges’ g (Hedges & Olkin, 1985), and d could be converted to Hedges’ g by using the correction factor J, corresponding to the following formula: J = 1 – (3/(4 df – 1) (Borenstein et al., 2014). The overall effect sizes were estimated by calculating a weighted average of individual effect sizes using a random effect model at 95% confidence intervals (CIs). Since the intervention studies were likely to differ in terms of sample characteristics, instructional features, and implementation of the programs, we selected a random effect model for estimating the effect. In the random effect model, the weighted average takes into account that the studies are associated with variations. Using this model is recommended by Borenstein et al. (2009).
Analyses of primary outcomes. To examine the difference in effect sizes between trained vocabulary and standardized vocabulary outcomes, we estimated two separate overall mean effect size – one for each outcome.
Multiple outcome reporting. When a study reported multiple indicators for the same type of outcome (e.g. multiple trained vocabulary outcomes or standardized vocabulary outcomes), the mean of the indicators was computed.
Multiple group comparisons. In one case (Silverman et al., 2013), several treatment groups compared with the same control group were reported. In this case, we computed the mean effect size from the study to avoid treating them as separate effects in the analyses.
Moderator analysis. Moderator analyses of training duration (total number of hours in the instruction programs) were conducted for the analyses of trained vocabulary and standardized vocabulary. The variable ‘training duration’ was originally planned to work as a continuous variable (Rogde et al., 2016); however, the variable was not normally distributed. For the analyses, the studies were therefore divided into those that included less than 30 hours of instruction and those that reported instruction for 30 hours or more.
The screening resulted in 17 included studies that reported both outcomes of trained vocabulary and standardized vocabulary. The studies involving preschool and school-aged children were included. An overview of the study characteristics is provided in Appendix 4. In all 26 papers were excluded. This mainly entailed papers that did not report taught vocabulary outcomes. In addition, studies that made use of several unit tests of taught vocabulary during the trial were excluded from this review to ensure that the two outcomes (taught vocabulary and standardized vocabulary) were measured at the same time.
Synthesis of results
Results for research question 1. To examine the size difference between gains in trained vocabulary and in standardized vocabulary, we computed two separate overall mean effect sizes of trained vocabulary and standardized vocabulary outcomes.
Figure 1 shows the 17 effect sizes comparing treatment and control groups on trained vocabulary outcomes (N treatment groups = 7492, mean sample size = 441, N controls = 5862, mean = 345). The mean effect size was large, g = 1.28, 95% CI = [0.95, 1.61], p = 0.0001. The heterogeneity among the studies was significant, Q(16) = 731.09, p = 0.0001, I² = 97.81, T² = 0.42. After removing two outliers (Coyne et al., 2010; Simmons et al., 2010), the mean effect was g = 0.85, (k = 15), 95% CI = [0.57, 1.13], p = 0.0001. The heterogeneity among the studies was significant, Q(14) = 481.04, p = 0.0001, I² = 97.09, T² = 0.27. In the protocol for the review by Rogde et al. (2019), outliers larger than three SDs from the mean should be excluded. It can be noted that the study by Clarke et al. (2010) also yielded a high effect size. Still, this study was closer to the mean effect size and judged to be at low risk of several biases on the quality assessment (see Appendix 4). It was therefore kept in the analyses.
Figure 2 shows the 17 effect sizes comparing treatment and control groups on standardized vocabulary outcomes (N treatment groups = 7492, mean sample size = 440, N controls = 5862, mean = 345). The mean effect size was negligible, g = 0.01, 95% CI = [-0.03, 0.04], p = 0.62, and there was no overlap between this CI and that for trained vocabulary. The heterogeneity among the studies was not significant, Q(16) = 14.21, p = 0.58, I² = 0.00, T² = 0.00. These results indicated a mean difference of g = 1.27 between standardized vocabulary outcomes and trained vocabulary outcomes. Taking into account the two outliers for the trained vocabulary, the difference between the two outcomes was still large, showing g = 0.84.
Results for research question 2: Moderator analysis of training duration. For the trained vocabulary outcomes, the effect sizes for the treatment groups with less than 30 hours of instruction [g = 3.17, 95% CI = 1.28 to 5.06, k = 5] were significantly larger than for the groups that received 30 hours of instruction or more [g = 0.86, 95% CI = 0.55 to 1.17, k = 12]. This indicated a pattern where less instruction time was associated with larger effect sizes. When removing the two outliers with very high effect sizes on trained vocabulary from the analysis (Coyne et al., 2010; Simmons et al., 2010), only three studies were left in the group with less than 30 hours of instruction, and there was no difference in the effect sizes related to the duration of instruction.
The overall effect on standardized vocabulary was close to zero, and the moderator analysis of training duration was not significant for this outcome.
Results for research question 3: Is there a relation between gains in trained words and in standardized measures?
We conducted a meta-regression analysis to examine whether standardized vocabulary skills could be mediated by trained vocabulary skills. The results showed that trained vocabulary was not a significant predictor of standardized vocabulary skills (β = 0.04, R² = 0.00, k = 17).
Results for research question 4: Is this relation stronger for expressive standardized tests than for receptive ones? Figure 3 shows the 10 reported expressive standardized tests. The results indicated a small mean effect size, g = 0.131, 95% CI = [0.03, 0.23], p = 0.01 for these studies. The heterogeneity among the studies was not significant, Q(9) = 8.998, p = 0.44, I² = 0.00, T² = 0.00. All eight studies that reported receptive standardized tests (Figure 4) showed a small mean effect size, g = 0.138, 95% CI = [0.03, 0.25], p = 0.02. The heterogeneity among the studies was not significant, Q(7) = 3.862, p = 0.80, I² = 0.00, T² = 0.00.
Two meta-regression analyses were conducted to examine whether standardized expressive or receptive vocabulary skills could be mediated by trained vocabulary skills. The results showed that trained vocabulary predicted neither standardized expressive vocabulary skills (β = 0.11, R² = 0.00, k = 10) nor standardized receptive vocabulary skills (β = -0.001, R² = 0.00, k = 8).
Risk of bias in the included studies
The risk of bias assessment (Appendix 3) showed that six studies were judged to be at high risk and eleven studies at low risk of selection bias. All studies represented a risk of performance bias, as blinding of personnel or participants are not possible in these type of trials. In all, seven studies reported blinding of the outcome assessment and were judged as low risk for detection bias; the remaining ten studies did not report whether or not the assessments were blinded and were categorized as being unclear. The assessment of attrition bias resulted in thirteen studies judged to be at low risk, one was judged unclear, and three were judged as high risk. None of the studies showed indications of reporting bias. No threshold was defined to exclude studies related to specific criteria for high risk of bias. This implies that all studies were included without any stratification incorporated into the conducted analyses. Thus, the risk of bias assessment was not incorporated in the mean analyses reported.
In this paper, our main aim is to examine the size of the difference between trained vocabulary and standardized vocabulary outcomes and how the duration of the instruction is associated with the two different outcome measures. The results support the first hypothesis in which the effect of vocabulary programs on trained vocabulary outcomes shows considerably larger effect sizes than on standardized vocabulary outcomes. The mean difference between the two types of outcome tests is more than 1 SD (g = 1.27). These results are in line with Slavin and Madden’s (2011) findings from other educational reviews that much larger positive effect sizes are associated with treatment-inherent measures in contrast to treatment-independent measures.
The results partly support the hypothesis that the training duration is likely to be differentially associated with trained vocabulary and standardized vocabulary outcomes. The effect sizes on trained vocabulary from treatment groups characterized by the fewest hours of training are associated with larger effects than effect sizes derived from treatment groups with more hours and more sessions of instruction. However, when two outliers showing very high values on the trained vocabulary outcomes are excluded from the analysis, no difference in effect sizes among the studies in relation to the hours of instruction is found. Due to the small number of studies and the use of categorical moderator variable analyses, this finding is in general not straightforward in its interpretation. It is also clear that the effect on trained vocabulary outcomes may be influenced by the number of trained words. A higher number of trained words could possibly be associated with smaller effect sizes. A limited number of trained words could reflect more repetition work or more elaborate learning strategies when the words are trained, thus leading to larger effect sizes. However, several studies do not report the total amount of trained words, and we have been unable to examine this issue further. As for the standardized vocabulary outcomes, it is not possible to detect any pattern of training duration as a moderator of effect sizes because the mean overall effect size is negligible. In conclusion, we can therefore not dismiss the possibility that these types of vocabulary outcomes may be differentially moderated by the duration of the instruction program.
Our second aim is to examine whether standardized outcomes would be mediated by the effects on trained words, as well as whether expressive and receptive outcomes would show similar or different associations with trained vocabulary. Contrary to the primitive elements theory (Taatgen, 2013) and the vector semantics theory (Jurafsky & Martin, 2014), we do not find any evidence of the relations between the effects on trained vocabulary and on standardized vocabulary measures. As opposed to earlier research showing the transfer of effects between trained vocabulary and standardized expressive measures (Melby-Lervåg et al., 2020), we do not find that trained vocabulary predicts effects on either receptive or expressive standardized measures of vocabulary. Thus, our findings support the theories by Bransford and Schwartz (1999) and Detterman (1993), suggesting that the method of testing the transfer is too restrictive to detect a possible relation. Alternatively, the results are in line with the findings of Singley and Anderson (1989) and Thorndike and Woodworth (1901) that this kind of transfer is quite rare and usually mainly occurs if the tasks are highly similar.
Why then do we not find any relation here between trained vocabulary and standardized measures (or more specifically, expressive standardized measures), as in the study of Melby-Lervåg et al. (2020)? It should be noted that the studies in this review vary in the kind of intervention programs used. Some studies focus mainly on the trained words and not on broader language skills; other studies have a broader approach and a longer duration. As outlined earlier, this moderator also seems to have at least a weak relation to the size of the effects on trained vocabulary versus standardized measures. In the study by Melby-Lervåg et al. (2020), which finds a relation between the sizes of the gains in trained vocabulary and in standardized expressive tests, the broader oral training is by far the largest part of the intervention. The intervention strength is also considerable, with 5 x 6 weeks over 1.5 years. Due to the few studies, it is not possible to enter the effects of expressive language, the effects on trained words, and training duration in one regression model. However, to examine more closely what actually improves in oral language interventions, these variables are important to consider in future studies.
The studies included in this review involved both RCTs and QEs with a pre-post controlled design. Based on the results of the quality assessment, the studies in the analyses represent studies with both low and high risk of selection bias which refers to processes of randomization and allocation. Since the analyses do not adjust for selection bias or other biases assessed, it is important to note that there are possible biases associated with the studies included in this review.
As indicated by our previous review (Rogde et al., 2019), there was no evidence of publication bias in the included studies. Despite the fact that missing studies always presents a possible source of biased conclusion in systematic reviews, the results from the p-curve analysis in Rogde et al. (2019) indicates that this is a true effect that is not limited by publication bias.
In conclusion, this study’s results highlight the importance of distinguishing between the interpretations of effect sizes associated with researcher-created tests of trained vocabulary and standardized vocabulary outcomes. The results support Slavin and Madden’s (2011) view that these types of outcomes should be differentiated when synthesizing effects in meta-analyses. Reviews that incorporate both types of outcomes but conduct separate analyses of them should also be precise in their interpretations of effects and their communication of evidence for practice relative to the types of outcomes to which they refer.
The current paper is written based upon additional analyses of a prior review published in 2019. The paper is based on a search conducted in 2018, and has not been updated for the current findings.
Analyses of publication bias have not been conducted exclusively for this paper, but the studies involved are included in a previous p-curve analysis of the standardized outcomes in the paper by Rogde et al. (2019). Effect sizes on taught vocabulary outcomes are likely to be larger than standardized outcomes. Thus, it is more likely that studies reporting taught vocabulary outcomes solely (i.e. and no standardized outcomes) are more biased to publication reporting than studies reporting both taught vocabulary and standardized outcomes (which are included in this review). Hence, we argue that publication bias analysis would be most important for the standardized outcomes in the studies, and since the studies in this review were already included in the analysis in the paper by Rogde et al. (2019), additional tests were not conducted for the purpose of this paper.
Declaration of conflicting interest
The authors have papers that are included in the review themselves.
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