What is meta-analysis? Meta-analysis is a research process used to systematically synthesise or merge the findings of single, independent studies, using statistical methods to calculate an overall or ‘absolute’ effect. 2 Meta-analysis does not simply pool data from smaller studies to achieve a larger sample size.
When does it make sense to perform a meta-analysis?
Meta-analyses have become an essential tool in synthesizing evidence on clinical and epidemiological questions derived from a multitude of similar studies assessing the particular issue. Appropriate and accessible statistical software is needed to produce the summary statistic of interest.
What are the disadvantages of doing a meta-analysis?
Limitations of Meta-analysis
- 3.1. Selection of Studies for the Meta-analysis. One of the primary goals of meta-analysis is to improve our understanding of organizational phenomena by combining all research evidence from multiple independent ...
- 3.2. Validity of included studies. ...
- 3.3. Small sample sizes. ...
- 3.4. Heterogeneity of methods and data analysis. ...
Why to perform a meta-analysis?
Why Perform A Meta - Analysis Evidence Based Research?
- The Need Of Performing Meta-Analysis: The validity of hypothesis cannot be based on outcomes of a single study. The results keep varying from one study to another.
- Steps To Perform A Meta-Analysis. Determine the question to be answered. Carry out a literature review. ...
- References: CChalmers, T.C., Matta, R.J., Smith, H. & Kunzler, A.M. ...
- Related Topics:
- Tags
How to write a meta analysis paper?
To minimize errors a good meta-analysis should follow simple steps like:
- Use two or more independent reviewers or have consensus meetings to decide about any conflicts
- Try to educate reviewers by making them practice analysis of the research by reading various articles so that every reviewer standardizes to a common goal
- Always indulge frequently in comparison of abstracts and texts to unearth discrepancies in the studies
What is a meta-analysis in simple terms?
What does meta-analysis mean? Meta-analysis is a statistical process that combines the data of multiple studies to find common results and to identify overall trends.
What is a meta approach?
Definition. A subset of systematic reviews; a method for systematically combining pertinent qualitative and quantitative study data from several selected studies to develop a single conclusion that has greater statistical power.
What is meta-analysis and example?
Meta-analysis refers to the statistical analysis of the data from independent primary studies focused on the same question, which aims to generate a quantitative estimate of the studied phenomenon, for example, the effectiveness of the intervention (Gopalakrishnan and Ganeshkumar, 2013).
What is a meta-analysis and why is it useful?
Meta-analysis is the use of statistical methods to summarize the results of independent studies. By combining information from all relevant studies, meta-analysis can provide more precise estimates of the effects of health care than those derived from the individual studies included within a review.
How do you write a meta-analysis method?
Eight steps in conducting a meta-analysisStep 1: defining the research question. ... Step 2: literature search. ... Step 3: choice of the effect size measure. ... Step 4: choice of the analytical method used. ... Step 5: choice of software. ... Step 6: coding of effect sizes. ... Step 7: analysis. ... Step 8: reporting results.
What's the difference between systematic review and meta-analysis?
A systematic review attempts to gather all available empirical research by using clearly defined, systematic methods to obtain answers to a specific question. A meta-analysis is the statistical process of analyzing and combining results from several similar studies.
What is another word for meta-analysis?
This is reflected in the variety of terms and definitions for synonym circumstances, e.g. "meta-analysis", "systematic review", "narrative review", "meta-syntheses".
What is a meta-analysis in qualitative research?
Qualitative meta-analysis is an attempt to conduct a rigorous secondary qualitative analysis of primary qualitative findings. Its purpose*to provide a more comprehensive description of a phenomenon and an assessment of the influence of the method of investigation on findings*is discussed.
How many studies is a meta-analysis?
Two studiesTwo studies is a sufficient number to perform a meta-analysis, provided that those two studies can be meaningfully pooled and provided their results are sufficiently 'similar'.
Is meta-analysis quantitative or qualitative?
Meta-analysis is a quantitative method that uses and synthesizes data from multiple individual studies to arrive at one or more conclusions. Meta-synthesis is another method that analyzes and combines data from multiple qualitative studies.
What is a disadvantage of meta-analysis?
Additionally, meta-analyses can be poorly executed. Carelessness in abstracting and summarizing appropriate studies, failure to consider important covariates, bias on the part of the meta-analyst and overstatements of the strength and precision of the results can all contribute to invalid meta-analyses.
What are the problems with meta-analysis?
Several problems arise in meta-analysis: regressions are often non-linear; effects are often multivariate rather than univariate; coverage can be restricted; bad studies may be included; the data summarised may not be homogeneous; grouping different causal factors may lead to meaningless estimates of effects; and the ...
What is a meta meeting?
Meta Meeting means the special meeting of Meta Shareholders, Meta Optionholders, Meta Warrantholders and holders of Meta DSUs including any adjournment or postponement thereof, to be called and held in accordance with the Interim Order to consider the Meta Arrangement Resolution, and for any other purpose as may be set ...
What is Meta Analytic Research?
Meta-analysis is a quantitative, formal, epidemiological study design used to systematically assess the results of previous research to derive conclusions about that body of research. Typically, but not necessarily, the study is based on randomized, controlled clinical trials.
Which of the following studies used a meta-analysis?
Van Ijzendoorn and Kroonenberg (1988)
Which of the following steps are aspects of meta-analysis and systematic reviews?
Identify research area interested in
How does publication bias affect the findings of the meta-analysis?
Increases validity
What is publication bias?
Publication bias is the idea research results that are insignificant or show results against the existing knowledge in psychology are less likely t...
How did Van Ijzendoorn and Kroonenberg’s (1988) research highlight the importance of meta-analysis in research?
This meta-analysis example shows the importance of meta-analysis in research as it allows the researchers to compare data from multiple countries i...
Why is a meta-analysis more likely to provide valid results than an independent study?
Meta-analyses draw conclusions based on evidence from multiple empirical sources. Therefore, there is an increased likelihood meta-analysis finding...
Why are meta-analyses more likely to be generalisable than an independent study?
Meta-analyses are likely to have larger samples than an independent study. Therefore, they are more likely to represent the population, which makes...
What are the steps taken in the meta-analysis methodology?
There are several stages of the meta-analysis methodology. These are: Identifying a research question and forming a hypothesis Creating an inclusio...
Why is meta analysis important?
Meta analyses are often used to take advantage of the large amount of research already available on a topic. Meta-analysis is a powerful tool for...
What does the term meta analysis mean?
The prefix "meta", which is Greek for transcending or beyond, is often used to describe the process of thinking about the bigger picture. In the c...
What is meta analysis example?
An example of a meta-analysis study would be a team of researchers collecting and statistically combining the results of 20 different randomized cl...
What is meta analysis in research?
A meta-analysis, sometimes referred to as a meta-analysis study, is a type of research which uses a systematic approach to statistically combine th...
What is the purpose of a meta-analysis?
The main task of the statistical model is to establish the properties of the effect-size population from which the individual effect-size estimates have been selected. To accomplish the first purpose in a meta-analysis, that is, to calculate an average effect size, two statistical models can be assumed: the fixed- and the random-effects models.
Why is meta analysis important?
Meta-analysis combines findings from many different – yet related – studies to foster empirical knowledge about causal associations that are more trustworthy than those possible from any single study. This benefit arises for two main reasons. First, combining findings from parallel studies promises to increase statistical power and precision for estimating the magnitude of a causal association. More importantly, however, is the potential of meta-analysis to strengthen external validity by identifying the realm of application of a causal association – that is, meta-analyses are most useful when they allow us to examine whether a causal association (1) holds with specific populations of persons, settings, times, and ways of varying the cause or measuring the effect; (2) holds across different populations of people, settings, times, and ways of operationalizing a cause and effect; and (3) can even be extrapolated to other populations of people, settings, times, causes, and effects than those that have been studied to date – that is, meta-analyses offer opportunities to probe external validity questions 1, 2, and 3.
What is the RR of a meta-analysis of 10 randomized control trials involving 1194 participants?
A meta-analysis of 10 randomized control trials involving 1194 participants showed no differences in the risk of preeclampsia [RR, 0.98; 95% CI, 0.56–1.74], a primary indicator of maternal outcome [13M ]. The meta-analysis included women with GDM who were not controlled with lifestyle modifications and thus required drug treatment. The treatment schedule in the control was insulin and the interventional group was glyburide [ 13M ]. This result was consistent with the findings of a previous meta-analysis of 11 studies that involved 1754 GDM patients [ 7M ].
What software is used to conduct meta analysis?
The statistics of meta-analysis could be conducted with software such as Stata or Review manager (RevMan).
What are the two types of statistical models used in meta-analysis?
To accomplish the first purpose in a meta-analysis, that is, to calculate an average effect size, two statistical models can be assumed: the fixed- and the random-effects models.
Why is literature search important?
Literature search is the first step, and is very important for meta-analysis, as incomplete literature search may bring incorrect results. •. Meta-analysis is done by identifying a common statistical measure that is shared among studies, and calculating a weighted average of that common measure. •.
What is meta analysis?
What is a meta-analysis? Meta-analysis is a statistical technique for combining data from multiple studies on a particular topic.
When did meta analysis start?
Meta-analyses began to appear as a leading part of research in the late 70s. Since then, they have become a common way for synthesizing evidence and summarizing the results of individual studies (2).
What is meta analysis?
A meta-analysis, sometimes referred to as a meta-analysis study, can be defined as a type of research which uses a statistical approach to combine the findings of numerous empirical studies into a summary study of available data on the given topic. Meta-analytical studies are often used when a large amount of scientific research has already been conducted on a certain topic, or research question. The researcher performing the meta-analysis is able to combine the existing data on the topic to produce a summative study that is more likely to have statistically significant results, or results that are able to reliably support that the difference between the control and test groups in a study was not simply caused by chance. In other words, meta-analyses are useful for averaging the results of many studies on a topic to show that there really is a cause-and-effect relationship between the factors being studied.
What is the overall analysis of meta-analysis?
After each study has been analyzed, the researcher will conduct an overall analysis to account for the different sample sizes of the individual studies in the meta-analysis . This overall approach, or combination of data from several studies into one, is referred to as aggregate data. Most commonly, a weighted-average analysis is used so that each study has the same influence on the overall results of the meta-study. To demonstrate, if a researcher used 30 studies in the meta-analysis and one of them used a sample size of 500 participants but the rest used less than 100 participants, the larger study would factor into the overall results more than the smaller studies. The data in a meta-analysis are often depicted with a type of graphic known as a forest plot.
Why is meta analysis important?
Meta-analysis is a powerful tool for increasing the amount of participant data available to answer a research question, increasing the reliability of the results , and providing summative answers to much-debated research questions.
What are the three types of bias in meta-analysis?
When interpreting the results of, or conducting, a meta-analysis study, there are three types of bias of which people should be aware: selection bias, reporting bias , and publication bias. Selection bias is particularly relevant in meta-analysis research because the researcher is responsible for selecting the studies which will be included in the meta-analysis study. Reporting bias occurs when a particular finding, or result, is omitted from the data set because it does not support the outcome that the researcher desires. The third type of bias, publication bias , occurs when the researcher, or research team, decides not to publish the results of a study because they do not support a certain theory or outcome.
What does meta mean in research?
The prefix "meta", which is Greek for transcending or beyond, is often used to describe the process of thinking about the bigger picture. In the case of meta-analysis, the researcher is using the big picture of studies available to think about a topic. The term meta-analysis means doing a study about studies.
How to validate meta-analysis?
Validating the results of a meta-analysis is normally achieved by testing the results for homogeneity. It is important to determine the degree to which the results of the studies being combined in a meta-analysis are similar, or homogenous. Homogeneity of results in a meta-analysis is desirable so that the data can be aggregated, or combined, without being adapted to meet the needs of the study. To determine homogeneity, researchers test for its opposite, heterogeneity. Cochran's-Q and I-Square, also called I-2 Index are two common statistical methods for determining heterogeneity of research findings.
What is the first step in meta-analysis?
The first step in meta-analysis involves carefully thinking about the topic in order to develop a precise research question.
What is meta analysis?
v. t. e. A meta-analysis is a statistical analysis that combines the results of multiple scientific studies. Meta-analysis can be performed when there are multiple scientific studies addressing the same question, with each individual study reporting measurements that are expected to have some degree of error.
Why is meta analysis important?
For instance, a meta-analysis may be conducted on several clinical trials of a medical treatment, in an effort to obtain a better understanding of how well the treatment works.
How does the quality effects meta-analysis work?
They introduced a new approach to adjustment for inter-study variability by incorporating the contribution of variance due to a relevant component (quality) in addition to the contribution of variance due to random error that is used in any fixed effects meta-analysis model to generate weights for each study. The strength of the quality effects meta-analysis is that it allows available methodological evidence to be used over subjective random effects, and thereby helps to close the damaging gap which has opened up between methodology and statistics in clinical research. To do this a synthetic bias variance is computed based on quality information to adjust inverse variance weights and the quality adjusted weight of the i th study is introduced. These adjusted weights are then used in meta-analysis. In other words, if study i is of good quality and other studies are of poor quality, a proportion of their quality adjusted weights is mathematically redistributed to study i giving it more weight towards the overall effect size. As studies become increasingly similar in terms of quality, re-distribution becomes progressively less and ceases when all studies are of equal quality (in the case of equal quality, the quality effects model defaults to the IVhet model – see previous section). A recent evaluation of the quality effects model (with some updates) demonstrates that despite the subjectivity of quality assessment, the performance (MSE and true variance under simulation) is superior to that achievable with the random effects model. This model thus replaces the untenable interpretations that abound in the literature and a software is available to explore this method further.
How does meta analysis affect results?
However, in performing a meta-analysis, an investigator must make choices which can affect the results, including deciding how to search for studies, selecting studies based on a set of objective criteria, dealing with incomplete data, analyzing the data, and accounting for or choosing not to account for publication bias. Judgment calls made in completing a meta-analysis may affect the results. For example, Wanous and colleagues examined four pairs of meta-analyses on the four topics of (a) job performance and satisfaction relationship, (b) realistic job previews, (c) correlates of role conflict and ambiguity, and (d) the job satisfaction and absenteeism relationship, and illustrated how various judgement calls made by the researchers produced different results.
Why do studies not report the effects?
Studies often do not report the effects when they do not reach statistical significance. For example, they may simply say that the groups did not show statistically significant differences, without reporting any other information (e.g. a statistic or p-value). Exclusion of these studies would lead to a situation similar to publication bias, but their inclusion (assuming null effects) would also bias the meta-analysis. MetaNSUE, a method created by Joaquim Radua, has shown to allow researchers to include unbiasedly these studies. Its steps are as follows:
What are the two types of evidence in meta-analysis?
In general, two types of evidence can be distinguished when performing a meta-analysis: individual participant data (IPD), and aggregate data (AD). The aggregate data can be direct or indirect.
Why are funnel plots controversial?
These are controversial because they typically have low power for detection of bias, but also may make false positives under some circumstances. For instance small study effects (biased smaller studies), wherein methodological differences between smaller and larger studies exist, may cause asymmetry in effect sizes that resembles publication bias. However, small study effects may be just as problematic for the interpretation of meta-analyses, and the imperative is on meta-analytic authors to investigate potential sources of bias.
What is the purpose of meta analysis?
The purpose of meta-analysis is that it seeks to determine whether an effect is present in a study and also determine whether the present effect is a positive one or a negative one. Meta-analysis examines the strengths of the results of a study. It checks whether there is substantial evidence to back up the findings of a study.
Why Meta-Analysis?
Meta-analysis is designed to review the information and put it into simpler terms. Meta-analysis however follows some principles which are:
Why is meta analysis important?
Meta-analysis is helpful if your studies are based on finding the similarity in the Trent between existing research and the new one. However, be mindful of the studies you combine so that your research will not be at risk of biases which can lead to erroneous conclusions.
What is the first step in conducting a research?
Step One: The first step is to develop the objective of the research in the form of a hypothesis or questions. This should be done before conducting day research to reduce the risk of insignificant variables appearing in the study.
Why is meta analysis so time consuming?
Meta-analysis is time-consuming. This is because it reviews outcomes from diverse studies.
What is the ability to be totally objective in analyzing and evaluating research outcomes?
Meta-Analysis has the ability to be totally objective in analyzing and evaluating research outcomes.
Why is meta-analysis best avoided?
If there are no similarities in the subjects of study meta-analysis is best avoided because the study may lose its meaning.
What is meta analysis?
What is meta-analysis? Meta-analysis is a research process used to systematically synthesise or merge the findings of single, independent studies, using statistical methods to calculate an overall or ‘absolute’ effect. 2 Meta-analysis does not simply pool data from smaller studies to achieve a larger sample size.
What are the outcomes of a meta-analysis?
Depending on the study and the research question, outcome measures could include numerical measures or categorical measures. For example, differences in scores on a questionnaire or differences in a measurement level such as blood pressure would be reported as a numerical mean. However, differences in the likelihood of being in one category versus another (eg, vaginal birth versus cesarean birth) are usually reported in terms of risk measures such as OR or relative risk (RR).
Why is meta analysis important in nursing?
Many Evidence Based Nursing commentaries feature recently published systematic review and meta-analysis because they not only bring new insight or strength to recommendations about the most effective healthcare practices but they also identify where future research should be directed to bridge the gaps or limitations in current evidence. The strength of conclusions from meta-analysis largely depends on the quality of the data available for synthesis. This reflects the quality of individual studies and the systematic review. Meta-analysis does not magically resolve the problem of underpowered or poorly designed studies and clinicians can be frustrated to find that even when a meta-analysis has been conducted, all that the researchers can conclude is that the evidence is weak, there is uncertainty about the effects of treatment and that higher quality research is needed to better inform practice. This is still an important finding and can inform our practice and challenge us to fill the evidence gaps with better quality research in the future.
What are outcome measures?
Depending on the study and the research question, outcome measures could include numerical measures or categorical measures. For example, differences in scores on a questionnaire or differences in a measurement level such as blood pressure would be reported as a numerical mean.
How many steps are there in metaanalysis?
There is debate about the best practice for meta-analysis, however there are five common steps.
Why is systematic review important?
A systematic review (SR) is specifically designed to address the research question and conducted to identify all studies considered to be both relevant and of sufficiently good quality to warrant inclusion. Often, only studies published in established journals are identified, but identification of ‘unpublished’ data is important to avoid ‘publication bias’ or exclusion of studies with negative findings. 4 Some meta-analyses only consider randomised control trials (RCTs) in the quest for highest quality evidence. Other types of ‘experimental’ and ‘quasi-experimental’ studies may be included if they satisfy the defined inclusion/exclusion criteria.
What is meta analysis?from study.com
meta analysis. A method that uses statistical techniques to combine results from different studies and obtain a quantitative estimate of the overall effect of a particular intervention or variable on a defined outcome —i.e., it is a statistical process for pooling data from many clinical trials to glean a clear answer.
How does the quality effects meta-analysis work?from en.wikipedia.org
They introduced a new approach to adjustment for inter-study variability by incorporating the contribution of variance due to a relevant component (quality) in addition to the contribution of variance due to random error that is used in any fixed effects meta-analysis model to generate weights for each study. The strength of the quality effects meta-analysis is that it allows available methodological evidence to be used over subjective random effects, and thereby helps to close the damaging gap which has opened up between methodology and statistics in clinical research. To do this a synthetic bias variance is computed based on quality information to adjust inverse variance weights and the quality adjusted weight of the i th study is introduced. These adjusted weights are then used in meta-analysis. In other words, if study i is of good quality and other studies are of poor quality, a proportion of their quality adjusted weights is mathematically redistributed to study i giving it more weight towards the overall effect size. As studies become increasingly similar in terms of quality, re-distribution becomes progressively less and ceases when all studies are of equal quality (in the case of equal quality, the quality effects model defaults to the IVhet model – see previous section). A recent evaluation of the quality effects model (with some updates) demonstrates that despite the subjectivity of quality assessment, the performance (MSE and true variance under simulation) is superior to that achievable with the random effects model. This model thus replaces the untenable interpretations that abound in the literature and a software is available to explore this method further.
What are some examples of meta-analysis?from study.com
Examples of a meta-analysis include statistically combining the results of many different clinical trials on the cardiovascular benefits of taking daily aspirin for people at risk of heart disease and performing a statistical analysis of the findings from a large number of studies regarding the academic performance of elementary students enrolled in virtual school compared with those who attend in-person. A meta-analysis is a powerful tool for increasing the amount of participant data available to answer a research question, increasing the reliability of the results, and providing summative answers to much-debated research questions.
What is the RR of a meta-analysis of 10 randomized control trials involving 1194 participants?from sciencedirect.com
A meta-analysis of 10 randomized control trials involving 1194 participants showed no differences in the risk of preeclampsia [RR, 0.98; 95% CI, 0.56–1.74], a primary indicator of maternal outcome [13M ]. The meta-analysis included women with GDM who were not controlled with lifestyle modifications and thus required drug treatment. The treatment schedule in the control was insulin and the interventional group was glyburide [ 13M ]. This result was consistent with the findings of a previous meta-analysis of 11 studies that involved 1754 GDM patients [ 7M ].
How to validate meta-analysis?from study.com
Validating the results of a meta-analysis is normally achieved by testing the results for homogeneity. It is important to determine the degree to which the results of the studies being combined in a meta-analysis are similar, or homogenous. Homogeneity of results in a meta-analysis is desirable so that the data can be aggregated, or combined, without being adapted to meet the needs of the study. To determine homogeneity, researchers test for its opposite, heterogeneity. Cochran's-Q and I-Square, also called I-2 Index are two common statistical methods for determining heterogeneity of research findings.
How does meta analysis affect results?from en.wikipedia.org
However, in performing a meta-analysis, an investigator must make choices which can affect the results, including deciding how to search for studies, selecting studies based on a set of objective criteria, dealing with incomplete data, analyzing the data, and accounting for or choosing not to account for publication bias. Judgment calls made in completing a meta-analysis may affect the results. For example, Wanous and colleagues examined four pairs of meta-analyses on the four topics of (a) job performance and satisfaction relationship, (b) realistic job previews, (c) correlates of role conflict and ambiguity, and (d) the job satisfaction and absenteeism relationship, and illustrated how various judgement calls made by the researchers produced different results.
What software is used to conduct meta analysis?from sciencedirect.com
The statistics of meta-analysis could be conducted with software such as Stata or Review manager (RevMan).
What are the advantages of meta analysis?
Meta-analysis now offers the opportunity to critically evaluate and statistic ally combine results of comparable studies or trials. Its major purposes are to increase the numbers of observations and the statistical power, and to improve the estimates ...
What are the advantages and disadvantages of meta analysis?
Advantages and disadvantages of the meta-analysis approach. Meta-analysis is superior to narrative reports for systematic reviews of the literature, but its quantitative results should be interpreted with caution even when the analysis is performed according to rigorous rules. Meta-analysis is superior to narrative reports for systematic reviews ...
What is sensitivity analysis?
Meta-analysts disagree on the criteria for inclusion or exclusion of primary studies, with relation to publication status, comparability and required scientific quality, but sensitivity analyses make it possible to assess the impact of various selection criteria on the results. Several statistical methods have been developed to analyse data ...
Overview
Challenges
A meta-analysis of several small studies does not always predict the results of a single large study. Some have argued that a weakness of the method is that sources of bias are not controlled by the method: a good meta-analysis cannot correct for poor design or bias in the original studies. This would mean that only methodologically sound studies should be included in a meta-analysis, a practi…
History
The historical roots of meta-analysis can be traced back to 17th century studies of astronomy, while a paper published in 1904 by the statistician Karl Pearson in the British Medical Journal which collated data from several studies of typhoid inoculation is seen as the first time a meta-analytic approach was used to aggregate the outcomes of multiple clinical studies. The first meta-analysis of all conceptually identical experiments concerning a particular research issue, and conducted …
Steps in a meta-analysis
A meta-analysis is usually preceded by a systematic review, as this allows identification and critical appraisal of all the relevant evidence (thereby limiting the risk of bias in summary estimates). The general steps are then as follows:
1. Formulation of the research question, e.g. using the PICO model (Population, Intervention, Comparison, Outcome).
Methods and assumptions
Applications in modern science
Modern statistical meta-analysis does more than just combine the effect sizes of a set of studies using a weighted average. It can test if the outcomes of studies show more variation than the variation that is expected because of the sampling of different numbers of research participants. Additionally, study characteristics such as measurement instrument used, population sampled, or aspects of the studies' design can be coded and used to reduce variance of the estimator (see s…
See also
• Estimation statistics
• Metascience
• Newcastle–Ottawa scale
• Reporting bias
• Review journal
Further reading
• Cornell JE, Mulrow CD (1999). "Meta-analysis". In Mellenbergh GJ (ed.). Research methodology in the life, behavioural, and social sciences. London: SAGE. pp. 285–323. ISBN 978-0-7619-5883-3.
• Ellis PD (2010). The Essential Guide to Effect Sizes: An Introduction to Statistical Power, Meta-Analysis and the Interpretation of Research Results. Cambridge: Cambridge University Press. ISBN 978-0-521-14246-5.