Meta-analysis is a statistical technique for amalgamating,
summarising, and reviewing previous quantitative
research. By using meta-analysis, a wide variety of questions can be
investigated, as long as a reasonable body of primary research studies exist.
Selected parts of the reported results of primary studies are entered into
a database, and this "meta-data" is "meta-analyzed", in similar ways to
working with other data - descriptively and then inferentially to test certain
Meta analysis can be used as a guide to answer the
question 'does what we are doing make a difference to X?', even if 'X' has
been measured using different instruments across a range of different
people. Meta-analysis provides a systematic overview of quantitative
research which has examined a particular question.
The appeal of meta analysis is that it in effect combines
all the research on one topic into one large study with many participants.
The danger is that in amalgamating a large set of different studies the
construct definitions can become imprecise and the results difficult to
Not surprisingly, as with any research technique, meta-analysis has its
advantages and disadvantages. An advantage is its objectivity, and yet like any research, ultimately its value depends on
making some qualitative-type contextualizations and understandings of the
- Meta-analysis has been used to give helpful insight into:
- the overall effectiveness of interventions (e.g., psychotherapy, outdoor education),
- the relative impact of independent variables (e.g., the effect of different types of therapy), and
- the strength of relationship between variables.
- To get more introduction to meta-analysis, go to
Effect Sizes & Confidence
Meta analysis reports findings in terms of effect sizes. The effect
size provides information about how much change is evident across all
studies and for subsets of studies.
- There are many different types of effect size, but they fall into
two main types:
standardized mean difference (e.g., Cohen's d or
Hedges g) or
correlation (e.g., Pearson's r)
- It is possible to convert one effect size into another, so each
really just offers a differently scaled measure of the strength of an
effect or a relationship.
- The standardised mean effect size is basically computed as the
difference score divided by the standard deviation of the scores.
- In meta-analysis, effect sizes should also be reported with:
- the number of studies and the number of effects used to create the
- confidence intervals to help readers determine the consistency and reliability
of the mean estimated effect size.
For more information about calculating effect sizes and
confidence intervals, see:
Tests of statistical significance can also be conducted and on the effect
Different effect sizes are calculated for different constructs of
interest, as predetermined by the researchers based on what issues are of
interest in the research literature.
Rules of thumb and comparisons with
field-specific benchmarks can be used to interpret effect sizes.
According to an arbitrary but commonly used interpretation of effect size
by Cohen (1988), a standardised mean effect size of 0 means no change,
negative effect sizes mean a negative change, with .2 a small change, .5 a
moderate change, and .8 a large charge. Wolf (1986), on the other hand,
suggests that .25 is educationally significant and .50 is clinically
Using Effect Sizes in Primary Studies
Meta-analysis methodologies, particularly effect sizes, are
also applicable to primary research. For example, effect sizes are particularly useful in
program evaluation studies. For more information:
How to Conduct
Meta-analytic Studies of
Hattie, J. (1992). Self-concept.
NJ: Lawrence Erlbaum.
Lipsey, M. W., & Wilson, D. B. (1993). The
efficacy of psychological, educational, and behavioral treatment.
American Psychologist, 48, 1181-1201.
Smith, M. L., Glass, G. V., & Miller, T.
I. (1980). The benefits of psychotherapy. Baltimore: Johns Hopkins University Press.
Meta-analysis Methodology References
Bushman, B. J., & Wells, G. L. (2001).
Narrative impressions of literature: The availability bias and the
corrective properties of meta-analytic approaches. Personality and
Social Psychology Bulletin, 27, 1123-1130.
Cohen, J. (1988). Statistical power
analysis for the behavioral sciences (2nd ed.). New York: Academic
Glass, G. V. (1976). Primary, secondary, and
meta-analysis of research. Educational Researcher, 5, 3-8.
Glass, G. V. (1977). Integrating findings:
The meta-analysis of research. Review of Research in Education, 5,
Wolf, F. M. (1986). Meta-analysis:
Quantitative methods for research synthesis. Beverly Hills, CA: Sage.