This blog post is part of our Research Article of the Month series. For this month, we highlight “A Meta-Analysis of Phonemic Awareness Instruction Provided to Children Suspected of Having a Reading Disability,” an article published in the journal Language, Speech, and Hearing Services in Schools in 2022. Important words related to research are bolded, and definitions of these terms are included at the end of the article in the “Terms to Know” section.
Why Did We Pick This Paper?
Phonemic awareness is the understanding of, and the ability to manipulate, the individual sounds in words. This ability has been linked to word decoding skills and long-term reading achievement. Thus, in order to prevent future reading disabilities, understanding how children suspected of having a reading disability respond to phonemic awareness instruction is critical. This study sheds light on who benefits most from phonemic awareness instruction and which instructional conditions (e.g., group size, intervention provider, etc.) produce the greatest effects on student reading outcomes. This study is a large-scale meta-analysis; the researchers synthesized data from 138 studies, ensuring broad coverage of existing research and empirical evidence on the topic.
What Are the Research Questions or Purpose?
This study examines the effects of phonemic awareness instruction on children suspected of having a reading disability.
What Methodology Do the Authors Employ?
The researchers conducted a meta-analysis of 138 studies on phonemic awareness instruction with children suspected of having a reading disability.
To be included in the review, the studies needed to:
- Investigate the effects of phonemic awareness instruction
- Report original data collected using an experimental or quasi-experimental design
- Focus on children under 18 with intact sensory abilities who were not reading at the levels expected for their age
- Be published in a peer-reviewed journal or available as an accepted thesis or dissertation
- Report relevant effect sizes
The researchers searched seven databases to identify all studies that met these criteria. The researchers assessed all included studies for quality by identifying potential threats to validity.
For each of the included studies, researchers examined the students’ performance on phonemic awareness skills such as segmentation (pulling sounds in a word apart), blending (putting sounds together to form a word), deletion (removing a sound in a word), and first sound identification (naming the first sound in a word, also referred to as FSID), as well as a composite measure of phonological awareness (understanding the structure of sound in language).
Researchers also took into account other variables in the studies that could affect the outcomes of phonemic awareness interventions. These variables included:
- Socioeconomic status of students
- Students' age
- Students' grade
- Intervention type (phonological awareness only, phonemic awareness only, phonological awareness and other literacy skills, or phonemic awareness and other literacy skills)
- Intervention provider (speech-language pathologist, teacher, computer, parent, peer, researcher, combination, or other)
- Delivery mode (individual, small group, large group, combination)
- Number of phonemic awareness skills taught
- Whether graphemes, the symbols representing sounds, were included in instruction
By tracking these variables across 138 studies, the researchers were able to use a random effects model to examine the relationships between intervention characteristics and student outcomes. Estimates of the average effect size of the interventions on student outcomes were reported. In addition, the researchers calculated and reported statistical heterogeneity within the dataset through subgroup analyses.
What Are the Key Findings?
Phonemic awareness instruction was found to have a positive effect on phonological and/or phonemic awareness composite outcomes. For individual student outcome measures (segmentation, blending, deletion, and FSID), the size of the effect varied. Phonemic awareness instruction showed moderate effects on composite (g = 0.511) and segmentation (g = 0.571) outcomes, while its influence was less pronounced on blending (g = 0.341), first sound identification (g = 0.428), and deletion (g = 0.248).
Phonemic awareness instruction can be effective for children of various ages. For example, children in kindergarten through first grade received the most benefit from phonemic awareness instruction, especially in regard to segmentation, blending, and first sound identification—the intervention showed a medium effect (ranging from 0.164 to 0.587) on these outcomes. Although the overall effect of phonemic awareness instruction tended to decline as students aged, children in later grades still experienced positive effects.
Phonemic awareness instruction was found to be effective when delivered by a variety of providers, including teachers, speech-language pathologists (SLPs), trained volunteers, and computers, though phonemic awareness interventions delivered by SLPs tended to have the largest effect (g = 0.914) on composite student outcome.
No statistically significant differences were found between instructional settings (individual, small-group, or large-group), indicating that all arrangements could be effective in delivering positive outcomes.
More instruction (longer individual sessions, higher frequency of sessions, or a longer duration of the intervention period) did not always lead to better outcomes. This suggests that the productivity of intervention sessions and the components of instruction have a significant impact on student learning beyond the amount of instruction alone.
Including graphemes in phonemic awareness instruction was associated with statistically significant gains in composite outcomes (g = 0.545). This finding suggests that graphemes should be incorporated into instruction as appropriate in accordance with the child’s knowledge of sound-symbol correspondence.
What Are the Limitations of This Paper?
Although this meta-analysis includes 138 studies with varying sample sizes, the inclusion of studies with a small number of participants could be seen as a limitation. This is because a small sample size can limit the statistical power of detecting effect sizes. Therefore, the findings of the subgroup analyses in this study should be interpreted with caution, as the results may not represent the full range of effects that could be observed through studies with more participants.
Another notable limitation, as described by the researchers, is their approach of using multiple outcomes (segmentation, blending, deletion, FSID, and composite) from each study instead of combining them into a single effect for each study. This decision might skew the results because, when multiple outcomes from a single study are treated as independent data points, this can lead to an overrepresentation of that study's effects. This could potentially result in biased estimates of the effect of the interventions on student reading outcomes. The presence of the considerable statistical heterogeneity identified between the different studies combined in this meta-analysis also implies that the overall effect sizes of the intervention should be interpreted with caution.
Terms to Know
- Meta-analysis: A meta-analysis synthesizes the results of separate studies addressing the same research question by systematically identifying and evaluating studies on a certain phenomenon, pooling their data and conducting statistical analyses, and interpreting the collective results.
- Experimental: Experimental research aims to determine whether a certain treatment influences a measurable outcome—for example, whether a certain instructional method influences students’ reading comprehension scores. To do this, participants are divided into two groups: an experimental group, which receives the treatment, and a control group, which does not receive the treatment. In an experimental study, these groups are randomly assigned, meaning each participant has equal probability of being in either the treatment or the control group. Both groups are tested before and after the treatment, and their results are compared.
- Quasi-experimental: A quasi-experimental study is similar to an experimental study except that participants are not randomly assigned to groups. In educational research, groups often are assigned by classroom rather than through random assignment, making this kind of research quasi-experimental.
- Peer-reviewed journal: When an author submits an article to a peer-reviewed journal, the article is reviewed by scholars in the field. They make sure that the article is accurate, relevant, high quality, and well written.
- Effect sizes: In statistics, effect size is a measure of the strength of the relationship between two variables in statistical analyses. A commonly used interpretation is to refer to effect size as small (g = 0.2), medium (g = 0.5), and large (g = 0.8) based on the benchmarks suggested by Cohen (1988), where “g” refers to Hedge’s g, a statistical measure of effect size.
- Validity: In research, validity refers to the extent to which a test or measurement tool measures what it intends to measure.
- Random effects model: A random effects model is a type of statistical model that measures how an independent variable affects a dependent variable across a number of different samples or studies. Unlike a fixed effects model, a random effects model accounts for variability between different groups in a dataset.
- Statistical heterogeneity: Statistical heterogeneity refers to the variation in study outcomes among the different studies included in the meta-analysis, implying that there is variability in the dataset.
- Subgroup analyses: Subgroup analyses aim to determine whether the association between two variables (such as phonemic awareness instruction and student outcomes) differs depending on a third variable (such as student grade level).
Rehfeld, D. M., Kirkpatrick, M., O’Guinn, N., Renbarger, R. (2022). A meta-analysis of phonemic awareness instruction provided to children suspected of having a reading disability. Language, Speech, and Hearing Services in Schools, 53, 1177-1201. https://doi.org/10.1044/2022_LSHSS-21-00160
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Routledge Academic.