Why Do Teachers Matter? A Meta-Analytic Review of how Teacher Characteristics and Competencies Affect Students' Academic Achievement

Esther López-Martín, Belén Gutiérrez-de-Rozas, Ana María González-Benito, Eva Expósito-Casas
International Journal of Educational Research
Universidad Nacional de Educación a Distancia (UNED), Department of Methods of Research and Diagnosis in Education II, C/ Juan del Rosal, 14, 28040, Madrid, Spain

Table of Contents

Overall Summary

Study Background and Main Findings

This meta-analysis synthesizes 40 studies (202 effect sizes) to examine the relationship between teacher characteristics/competencies and secondary school students' academic performance. The overall mean effect size is 0.313 (R2 = 0.092, p < .001), indicating a statistically significant, moderate positive association. Key teacher characteristics with larger effect sizes include reflective attitude (Zr = 0.581), professional development (Zr = 0.426), and self-efficacy (Zr = 0.386). Moderator analyses reveal stronger effects in countries with a lower Human Development Index (HDI) and for studies reporting correlations rather than standardized beta coefficients. The study acknowledges limitations due to the reliance on correlational data.

Research Impact and Future Directions

The meta-analysis provides valuable insights into the relationship between teacher characteristics/competencies and secondary school students' academic performance, finding a statistically significant, moderate overall effect (Zr = 0.313, p < .001). However, it's crucial to distinguish between correlation and causation. The study primarily relies on correlational data, which limits the ability to infer a direct causal link between teacher attributes and student outcomes. While the study controls for some confounders, the possibility of other unmeasured variables influencing the results remains.

The practical utility of the findings is significant, particularly in highlighting the importance of specific teacher attributes like reflective attitude, professional development, and self-efficacy. These findings align with existing research emphasizing the role of teacher quality in student achievement. The study's identification of moderator variables, such as the country's HDI and the type of effect size reported, provides valuable context for understanding the variability in the observed effects. The stronger effects found in countries with lower HDIs suggest that targeted interventions focused on teacher development may be particularly impactful in those contexts.

While the study offers valuable guidance for educational practitioners and policymakers, emphasizing the importance of fostering reflective practice and providing opportunities for professional development, it also acknowledges key uncertainties. The high heterogeneity observed among the effect sizes suggests that the relationship between teacher characteristics and student achievement is complex and context-dependent. The study's limitations, including the reliance on correlational data and the potential for publication bias, necessitate a cautious interpretation of the findings.

Critical unanswered questions remain, particularly regarding the causal mechanisms linking teacher characteristics to student outcomes. The study's methodological limitations, especially the reliance on correlational data and the limited number of experimental studies, fundamentally affect the ability to draw strong causal conclusions. Future research should prioritize experimental and quasi-experimental designs to address these limitations and provide a more definitive understanding of the causal impact of specific teacher attributes on student achievement. Further investigation into the interplay between teacher characteristics, teaching practices, and contextual factors is also warranted.

Critical Analysis and Recommendations

Clear Research Objective (written-content)
The abstract clearly states the research objective: to conduct a meta-analysis on the relationship between teacher characteristics/competencies and student performance. This clarity is crucial for readers to quickly grasp the study's purpose.
Section: Abstract
Adherence to PRISMA Guidelines (written-content)
The Method section meticulously adheres to PRISMA guidelines, detailing the systematic review process. This enhances transparency and reproducibility, allowing other researchers to verify and build upon the findings.
Section: Method
Clear Presentation of Overall Effect Size (written-content)
The Results section clearly presents the overall mean effect size (Zr = 0.313, p < .001), indicating a statistically significant, moderate relationship. This provides a quantifiable measure of the association between teacher characteristics and student achievement.
Section: Results
Identification of Key Teacher Characteristics (written-content)
The study identifies specific teacher characteristics, such as reflective attitude (Zr = 0.581), professional development (Zr = 0.426), and self-efficacy (Zr = 0.386), with larger effect sizes. This provides valuable insights for targeted interventions aimed at improving teacher effectiveness.
Section: Results
Significant Moderator Effects (written-content)
Moderator analyses reveal that the effect of teacher characteristics is stronger in countries with a lower HDI. This highlights the importance of context and suggests that interventions may need to be tailored to specific settings.
Section: Results
Acknowledgment of Correlational Data Limitation (written-content)
The Discussion section acknowledges the limitation of relying primarily on correlational data. This prevents strong causal inferences, a critical point for interpreting the findings.
Section: Discussion
Missing Inter-rater Reliability for Coding (written-content)
The Method section does not report inter-rater reliability for the coding of variables. This omission weakens the assessment of the coding process's consistency and reliability.
Section: Method
Inconsistent Reporting of Test Statistics (written-content)
The Results section does not consistently report all test statistics (e.g., Z-values, F-values) for all comparisons. Including these statistics would provide a more complete picture of the results and facilitate comparisons with other studies.
Section: Results
Lack of Discussion of Alternative Explanations (written-content)
The Discussion section does not adequately discuss potential alternative explanations for the observed findings. Considering alternative interpretations would strengthen the critical evaluation of the results.
Section: Discussion

Section Analysis

Abstract

Key Aspects

Strengths

Suggestions for Improvement

Introduction

Key Aspects

Strengths

Suggestions for Improvement

Method

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Fig. 1. Flow diagram of study selection process.
Figure/Table Image (Page 5)
Fig. 1. Flow diagram of study selection process.
First Reference in Text
Following the PRISMA guidelines (Liberati et al., 2009; Moher et al., 2009), Fig. 1 shows the flow diagram of the entire selection process.
Description
  • Overview of the Flow Diagram: The flow diagram illustrates the process used to select studies for a meta-analysis. It starts with an initial pool of 'records identified through database searching' which amounts to 2042 articles, and zero additional records from other sources. Then, the diagram shows how this pool is reduced step-by-step as the researchers apply specific criteria to determine which studies are suitable for inclusion in the final meta-analysis. The process includes removing duplicates and articles in other languages, screening records based on title and abstract, assessing full-text articles for eligibility, and finally, including studies in qualitative and quantitative synthesis (meta-analysis). The diagram also indicates the number of records excluded at each stage, along with the reasons for exclusion.
  • Initial Screening: The diagram shows that after the initial search, 1453 records remained after duplicates, editorials, and other languages were removed. This indicates that a significant portion of the initial records were either duplicates or did not meet the language or format criteria.
  • Title/Abstract Screening: Following the screening of titles and abstracts, 1349 records were excluded, leaving 104 full-text articles to be assessed for eligibility. The primary reason for exclusion at this stage was topic irrelevance, accounting for 1206 exclusions.
  • Full-Text Assessment: After assessing the full-text articles, 64 were excluded, and the final quantitative synthesis (meta-analysis) included 40 studies. The main reasons for exclusion at this stage were no access to complete text (3), results published previously (2), DV different to academic achievement (10), IV different to teacher’s characteristic/competence (5), and do not provide correlations or standardized beta coefficient (44).
Scientific Validity
  • Adherence to PRISMA Guidelines: The flow diagram adheres to the PRISMA guidelines, which are an evidence-based set of items for reporting in systematic reviews and meta-analyses. This ensures that the study selection process is transparent and reproducible.
  • Audit Trail: The diagram provides a clear audit trail of the study selection process, which is crucial for assessing the potential for selection bias. By documenting the number of records excluded at each stage and the reasons for exclusion, the authors provide a clear rationale for their final sample of studies.
  • Exclusion Categories: The categories for reasons of exclusion appear comprehensive, covering the key reasons why studies might be excluded from a meta-analysis (e.g., topic irrelevance, study design, data availability). However, the "DV different to academic achievement" and "IV different to teacher's characteristic/competence" categories could potentially be more granular to provide further insight into the types of studies excluded.
Communication
  • Overall Clarity: The flow diagram clearly presents the study selection process, which enhances the transparency and reproducibility of the meta-analysis. The use of PRISMA guidelines is explicitly mentioned, providing context for the diagram's structure. The diagram is easy to follow, with clear labels and sequential steps.
  • Quantitative Information: The diagram effectively communicates the number of records at each stage of the selection process (e.g., records identified, records screened, full-text articles assessed). This quantitative data provides a clear understanding of the attrition rate and the reasons for exclusion.
  • Reasons for Exclusion: The reasons for excluding studies at each stage are clearly listed, which enhances the transparency of the selection process. This allows readers to understand the criteria used to select studies for inclusion in the meta-analysis.

Results

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Fig. 2. Fisher's transformation and confidence interval for studies that report...
Full Caption

Fig. 2. Fisher's transformation and confidence interval for studies that report greater and smaller effect sizes.

Figure/Table Image (Page 7)
Fig. 2. Fisher's transformation and confidence interval for studies that report greater and smaller effect sizes.
First Reference in Text
This is reflected in the forest plot (Fig. 2) that represents the 20 largest positive effects and the 20 largest negative effects, together with the confidence interval in each case.
Description
  • Overall Structure and Purpose: This figure is a forest plot, which is a graphical way to show the results of multiple scientific studies that address the same question. In this case, it shows the effects of teacher characteristics and competencies on student achievement. Instead of showing all the studies, this plot specifically highlights the 20 studies that found the largest positive effects (meaning teacher qualities had a big, beneficial impact) and the 20 studies that found the largest negative effects (meaning teacher qualities had a big, detrimental impact). The x-axis represents the effect size, which is a number that indicates the strength and direction of the relationship between teacher characteristics and student outcomes. The effect sizes have been transformed using Fisher's transformation, a statistical technique used to make the distribution of correlation coefficients more normal, which is important for meta-analysis. Each study is represented by a horizontal line, with the dot in the middle indicating the effect size and the line representing the confidence interval, which is a range of values within which the true effect size is likely to fall.
  • Interpretation of Horizontal Lines: Each horizontal line in the plot represents a single study. The position of the line on the x-axis indicates the study's effect size. Studies to the right of zero suggest a positive effect (teacher characteristics improving student achievement), while studies to the left of zero suggest a negative effect (teacher characteristics hindering student achievement).
  • Fisher's Transformation: The caption notes that a 'Fisher's transformation' has been applied. Fisher's z-transformation converts correlation coefficients (a measure of the strength and direction of a relationship between two variables) into z-scores, which are easier to work with in statistical analyses. This transformation is particularly useful when performing a meta-analysis, as it helps to normalize the distribution of correlation coefficients, making statistical inferences more accurate.
  • Confidence Intervals: Each study also has a confidence interval. A confidence interval (CI) provides a range of values within which the true effect size is likely to fall. Narrower confidence intervals indicate more precise estimates, while wider intervals indicate less precise estimates. If the confidence interval crosses zero, it means that the study's results are not statistically significant, as the true effect size could be zero (i.e., no effect).
Scientific Validity
  • Potential for Bias: Presenting only the largest positive and negative effects can be misleading, as it does not provide a complete picture of the distribution of effect sizes. This approach may overemphasize extreme findings and underrepresent the central tendency of the data. A standard forest plot showing all effect sizes would be more informative.
  • Clarity on Transformation: While the figure caption mentions Fisher's transformation, it does not explicitly state whether the confidence intervals are also based on this transformation. It is important to clarify this, as the interpretation of confidence intervals depends on the scale used.
  • Justification for Selection: The decision to present only the extreme effects should be justified. If the purpose is to illustrate the heterogeneity (variability) of the effect sizes, this should be explicitly stated. However, a more comprehensive analysis of heterogeneity, such as examining the I-squared statistic and conducting subgroup analyses, would be more appropriate.
Communication
  • Visual Representation of Effect Sizes: The forest plot provides a visual representation of the range of effect sizes observed in the included studies. By focusing on the 20 largest positive and 20 largest negative effects, the plot highlights the extreme ends of the distribution, which may be useful for illustrating the variability in the data. However, it omits the majority of effect sizes, potentially skewing the reader's perception of the overall effect.
  • Confidence Intervals: The inclusion of confidence intervals provides information about the precision of the effect size estimates for each study. Wider confidence intervals indicate less precise estimates, while narrower intervals indicate more precise estimates. This helps readers to assess the reliability of the individual study findings.
  • Study Labels: The study labels are somewhat truncated, making it difficult to identify the full study citation. The plot could benefit from a clearer labeling system, such as using a numerical index to refer to a table with full study details.
  • Caption Clarity: The caption is reasonably informative, but it could be improved by explicitly stating that the plot shows a selection of the largest positive and negative effects, rather than all effects.
Table 1 Mean Effect Size for Teacher Characteristics and Competencies on...
Full Caption

Table 1 Mean Effect Size for Teacher Characteristics and Competencies on Academic Output.

Figure/Table Image (Page 8)
Table 1 Mean Effect Size for Teacher Characteristics and Competencies on Academic Output.
First Reference in Text
Bearing this in mind, Table 1 shows the mean effect size of teacher characteristics and competencies on students' academic achievement, both globally and broken down into the general and specific categories in which the independent variable is codified.
Description
  • Global: This table presents a summary of how much different qualities and skills of teachers affect students' academic performance. It's organized into rows and columns, where each row represents a specific teacher characteristic or competence (like 'teaching competencies' or 'self-efficacy'), and the columns show different statistical measures that help us understand the effect size. The table shows the overall effect (
  • Categorization of Variables: The table breaks down the analysis into different categories of teacher characteristics and competencies. It distinguishes between 'Type of teacher characteristic or competence (Overall dimensions)' and 'Type of teacher characteristic or competence (Specific variables)'. The 'Overall dimensions' include broader categories like 'General description', 'Acquired characteristics', and 'Psychological characteristics'. The 'Specific variables' section provides a more detailed breakdown, such as 'Initial training', 'Professional experience', and 'Self-efficacy'.
  • Key Statistics: For each characteristic or competence, the table provides several key statistics. 'm' represents the number of studies that examined that particular characteristic. 'k' represents the number of effect sizes included for that characteristic. The effect size is a way to quantify the size of the effect, that is, the magnitude of the impact that the teacher characteristic has on the student's academic output. 'Zr' is Fisher's Z, a transformed correlation coefficient used to stabilize the variance and allow for more accurate statistical analysis. 'SE' is the standard error, which measures the precision of the estimated effect size. '95% CI' is the 95% confidence interval, which provides a range of values within which the true effect size is likely to fall. 'Z-test' is the test statistic for a Z-test, which is used to determine the statistical significance of the effect size. 'p-value' is the probability of observing the obtained results (or more extreme results) if there is no true effect.
  • Variance Components: The table also includes 'σₘ' and 'σₑ'. 'σₘ' represents the variance between studies, indicating the heterogeneity (variability) of the effect sizes across different studies. 'σₑ' represents the variance between effect sizes, indicating the variability of the effect sizes within studies.
  • Significance Levels: The table includes significance levels indicated by asterisks: *Statistically significant at confidence level of 90%, ** 95%, *** 99%.
Scientific Validity
  • Statistical Rigor: The table presents key statistics for each category and variable, including the number of studies (m), number of effect sizes (k), mean effect size (Zr), standard error (SE), confidence interval (CI), Z-test statistic, and p-value. This allows readers to assess the statistical significance and precision of the results.
  • Heterogeneity: The inclusion of variance components (σₘ and σₑ) provides information about the heterogeneity of the effect sizes. This is important for interpreting the results, as high heterogeneity may suggest that the effect sizes vary across different contexts or populations.
  • Effect Size Measure: The table uses Fisher's Z transformation (Zr) as the measure of effect size. While this is a common practice in meta-analysis, it is important to consider the limitations of this transformation and whether it is appropriate for the data.
  • Sample Size Threshold: The table includes a note cautioning readers about interpreting subcategories with effect sizes derived from less than five studies. This is a good practice, as estimates from small samples can be unreliable. However, it would be helpful to provide a more specific rationale for this threshold.
Communication
  • Overall Summary: The table effectively summarizes the mean effect sizes for different categories of teacher characteristics and competencies. The breakdown into overall dimensions and specific variables provides a comprehensive overview of the findings. The inclusion of confidence intervals and p-values allows readers to assess the statistical significance of the results.
  • Readability: The use of clear and concise labels for each category and variable enhances the readability of the table. The footnotes provide helpful explanations of the abbreviations used, such as 'CI' for confidence interval.
  • Caveats: The table includes a note cautioning readers about interpreting subcategories with effect sizes derived from less than five studies. This is a good practice, as estimates from small samples can be unreliable.
Table 2 Moderator Analyses for the Effects of Teacher Characteristics and...
Full Caption

Table 2 Moderator Analyses for the Effects of Teacher Characteristics and Competencies on Academic Performance.

Figure/Table Image (Page 9)
Table 2 Moderator Analyses for the Effects of Teacher Characteristics and Competencies on Academic Performance.
First Reference in Text
The results of the moderator analysis are included in Table 2.
Description
  • Overall Purpose and Structure: This table shows how certain factors might change the relationship between what teachers are like or can do (their characteristics and competencies) and how well their students perform in school (academic performance). These factors are called 'moderators' because they change or 'moderate' the size or direction of the relationship. For instance, one moderator is the 'Country' where the study took place, broken down by its Human Development Index (HDI).
  • List of Moderators: The table presents several moderators: 'Country' (categorized by low/medium HDI vs. high/very high HDI), 'Educational level' (primary and secondary education vs. secondary education), 'DV measurement' (standardized vs. non-standardized tests), 'DV category' (general achievement, mathematics, language arts, sciences, other curricular subjects), 'Type of effect' (correlation vs. beta), and 'IV information source' (students, teachers, schools). Each moderator is split into its different levels.
  • Statistical Measures: For each moderator and its levels, the table provides several key statistics: 'm' (number of studies), 'k' (number of effect sizes), 'F(DF)' (F-statistic and degrees of freedom from the Wald test, which tests whether the moderator significantly affects the relationship), 'σₘ' (variance between studies), 'Zr' (Fisher's Z, a measure of effect size), 'SE' (standard error), '95% CI' (95% confidence interval), 'Z-test' (Z-test statistic), and 'p-value' (probability value).
  • Interpretation of P-values: The p-values indicate the statistical significance of the moderator effect. For example, the p-value for 'Country' is less than 0.001, meaning that the country's HDI significantly moderates the relationship between teacher characteristics and student academic performance.
  • Significance Levels: The table includes significance levels indicated by asterisks: *Statistically significant at confidence level of 90%, ** 95%, *** 99%.
Scientific Validity
  • Statistical Methodology: The table presents the results of moderator analyses using a multilevel mixed-effects model, which is an appropriate approach for accounting for the nested structure of meta-analytic data (i.e., effect sizes within studies). The use of the Wald test to assess the significance of the moderator effects is also appropriate.
  • Choice of Moderators: The choice of moderators appears reasonable, based on theoretical considerations and previous research. However, the rationale for selecting these specific moderators should be explicitly stated in the methods section.
  • Degrees of Freedom: The degrees of freedom for the F-statistic are often fractional (e.g., F(1; 13.9)), which suggests the use of a Satterthwaite approximation or similar method to adjust the degrees of freedom for small sample sizes or unequal variances. This is a good practice, as it helps to ensure the accuracy of the p-values.
  • Sample Size Considerations: As with Table 1, the table includes a note cautioning readers about interpreting subcategories with effect sizes from less than five studies. This is a good practice, as estimates from small samples can be unreliable.
Communication
  • Overall Clarity and Readability: The table clearly presents the results of the moderator analyses, allowing readers to quickly identify which study characteristics significantly influence the relationship between teacher characteristics/competencies and student academic performance. The use of distinct rows for each moderator and its levels enhances readability.
  • Statistical Information: The inclusion of the F-statistic (or other appropriate test statistic), degrees of freedom, p-value, and effect size (Zr) for each moderator allows readers to assess the statistical significance and magnitude of the moderating effect. Providing the 95% confidence intervals for Zr further aids in interpreting the results.
  • Caveats and Consistency: The table includes a note cautioning readers about interpreting subcategories with effect sizes from less than five studies. This is a good practice, as estimates from small samples can be unreliable. This is consistent with the note in Table 1.
Fig. 3. Funnel plots.
Figure/Table Image (Page 10)
Fig. 3. Funnel plots.
First Reference in Text
The funnel plots represented in Fig. 3 reflect a similar distribution to the effect sizes reported by studies conducted on larger, and those carried out on smaller samples.
Description
  • Funnel Plot Basics: A funnel plot is a graph used in meta-analysis to check for publication bias, which is the tendency for studies with statistically significant or positive results to be more likely to be published than studies with non-significant or negative results. The plot gets its name from its shape: if there is no publication bias, the studies should be distributed symmetrically around the mean effect size, forming a shape resembling an inverted funnel. The x-axis represents the effect size (in this case, Fisher's Z transformed correlation coefficient), and the y-axis represents the standard error, which is a measure of the precision of the effect size estimate.
  • Two Plots and Trim and Fill: The figure actually contains two funnel plots: one on the left and one on the right. The plot on the left shows the original data, while the plot on the right shows the data after applying the "trim and fill" method. The "trim and fill" method is a statistical technique used to estimate the number of missing studies due to publication bias and to adjust the overall effect size accordingly. The solid circles represent the original studies included in the meta-analysis, while the open circles represent the studies that were imputed by the "trim and fill" method.
  • Interpretation of Asymmetry: If there is publication bias, the funnel plot will be asymmetrical, with a gap in one of the bottom corners. This indicates that studies with small sample sizes and non-significant results are missing from the meta-analysis. In this case, the plots appear to be somewhat asymmetrical, with a gap on the right side, suggesting that there may be some publication bias.
Scientific Validity
  • Appropriateness of Method: The use of funnel plots is an appropriate method for visually assessing publication bias. However, visual inspection of funnel plots is subjective, and it is important to supplement this with statistical tests for publication bias.
  • Trim and Fill Method: The "trim and fill" method is a commonly used technique for adjusting for publication bias. However, it is important to acknowledge that this method makes assumptions about the missing data mechanism and may not always be appropriate.
  • Interpretation Clarity: The interpretation of the funnel plots is somewhat vague. The authors state that the plots "reflect a similar distribution to the effect sizes reported by studies conducted on larger, and those carried out on smaller samples." This statement is not very informative. A more specific interpretation of the asymmetry (or lack thereof) would be helpful.
Communication
  • Axes Labels and Visual Aids: The funnel plots are presented with clear axes labels, making it easy to understand the variables being plotted (Fisher's Z on the x-axis and Standard Error on the y-axis). The plots also include a visual aid (dotted line) representing the expected shape of the funnel, which helps to assess the presence of asymmetry.
  • Data Differentiation: The plots distinguish between original data and imputed data (using solid and open circles, respectively), which enhances transparency. However, the legend could be more prominent to ensure that readers notice this distinction.
  • Caption Informativeness: The caption is concise but could be more informative. It should explicitly state that the funnel plots are used to assess publication bias and that the "trim and fill" method was applied.
Fig. 4. Mean effect size relative to the quality of the publication.
Figure/Table Image (Page 10)
Fig. 4. Mean effect size relative to the quality of the publication.
First Reference in Text
Fig. 4 shows the mean effect size estimated for studies relative to the quality of the publication.
Description
  • Overall Purpose: This figure is a plot that compares the average effect sizes from studies based on where they were published, as a proxy for publication quality. The researchers divided the studies into three groups: those published in journals with an impact factor in Journal Citation Reports (JCR), those published in journals with an impact factor in Scimago Journal & Country Rank (SJR) but not JCR, and those published in journals not included in either JCR or SJR. JCR and SJR are different systems that rank scientific journals based on how often their articles are cited by other researchers, with higher rankings generally indicating more prestigious and influential journals.
  • Plot Elements: The plot shows the mean (average) effect size for each of these three groups, with horizontal lines representing the mean and error bars representing the standard error. The standard error indicates the precision of the mean estimate; smaller error bars indicate more precise estimates, while larger error bars indicate less precise estimates.
  • Key Values: The figure shows that the mean effect size is highest for studies published in journals not indexed in JCR or SJR (0.52 ± 0.26), followed by studies published in journals indexed in SJR (0.35 ± 0.17), and lowest for studies published in journals indexed in JCR (0.22 ± 0.10). However, as stated in the main text, these differences are not statistically significant.
Scientific Validity
  • Proxy for Publication Quality: Using journal indexing (JCR and SJR) as a proxy for publication quality is a reasonable approach, as these indices are widely used and reflect the impact and visibility of journals. However, it is important to acknowledge that these indices are not perfect measures of quality, and other factors, such as methodological rigor and reporting standards, may also be important.
  • Missing Sample Sizes: The figure presents the mean effect size and standard error for each category. However, it does not provide information about the number of studies included in each category. This information is important for assessing the reliability of the estimates.
  • Statistical Significance: The authors should explicitly acknowledge that the differences in mean effect sizes across the publication quality categories were found to be statistically non-significant based on the Wald test. This is important for avoiding overinterpretation of the results.
Communication
  • Visual Clarity: The figure presents a clear visual comparison of the mean effect sizes across different categories of publication quality. The use of error bars provides information about the uncertainty associated with each estimate.
  • Labeling: The figure uses readily understandable labels for the publication quality categories (JCR, SJR, Others). However, it could be helpful to explicitly define these categories in the figure caption or a footnote.
  • Key Trend: The figure effectively communicates the trend that studies published in journals not indexed in JCR or SJR tend to report higher effect sizes. However, it is important to note that the Wald test found these differences to be statistically non-significant.

Discussion

Key Aspects

Strengths

Suggestions for Improvement

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