Meta-Analysis in SPSS Assignment Help — Effect Sizes and Heterogeneity

What Is Meta-Analysis and When Do You Use It?

Meta-analysis statistically synthesises effect sizes from multiple independent studies on the same research question into a single, pooled estimate. Rather than analysing a single dataset, your “data points” are the effect sizes reported in each included study. In SPSS, this is typically done via dedicated meta-analysis macros rather than a built-in menu procedure. Use it when your assignment requires combining findings across several published studies rather than analysing primary data you collected yourself.

If you’re analysing your own dataset directly, this isn’t the right method. Meta-analysis specifically operates on the results of other studies.

Assumptions You Must Check Before Running It in SPSS

• The right effect size type for your outcome. Continuous outcomes typically use Cohen’s d or Hedges’ g (Hedges’ g corrects for small-sample bias and is often preferred); binary outcomes use odds ratios or risk ratios. Mixing these without conversion produces an invalid pooled estimate.
• Assess heterogeneity before choosing a model. The I² statistic quantifies what proportion of variability across studies reflects real differences rather than chance: low I² (under 25%) supports a fixed-effects model (assumes one true effect size); higher I² supports a random-effects model (assumes true effect size varies across studies).
• Check for publication bias, since studies with null results are less likely to be published, which can inflate the pooled effect.

How to Run It in SPSS (Step by Step)

1. Build a dataset where each row is a study, with columns for the study’s effect size (or the raw numbers needed to calculate it), sample size, and standard error/variance.
2. Install and run an appropriate SPSS meta-analysis macro (several are available for combining effect sizes, testing heterogeneity, and producing forest plots).
3. Specify whether to pool using a fixed-effects or random-effects model, based on your I² result.
4. Request a forest plot to visualise each study’s effect size and the pooled estimate, and a funnel plot to assess publication bias.

How to Interpret the Output

1. Check the I² statistic and its accompanying Q-test for heterogeneity to confirm your choice of fixed vs random-effects model.
2. Read the pooled effect size and its confidence interval: the headline result of the meta-analysis.
3. Inspect the forest plot: each study’s effect size and CI as a horizontal line, with the pooled estimate as a diamond at the bottom.
4. Assess publication bias via funnel plot asymmetry and Egger’s test (a significant Egger’s test suggests possible bias).

How to Report the Results in APA Format

A random-effects meta-analysis of 14 studies (total N = 2,340) found a significant pooled effect of the intervention, Hedges’ g = 0.45, 95% CI [0.28, 0.62], p < .001. Substantial heterogeneity was observed, I² = 68%, Q(13) = 40.52, p < .001. Egger’s test showed no significant evidence of publication bias, p = .312.

Fixed-Effects vs Random-Effects Meta-Analysis: Understanding the Difference

A fixed-effects model assumes every included study is estimating the same true underlying effect, with observed differences purely due to sampling error, appropriate when heterogeneity (I²) is low. A random-effects model assumes the true effect genuinely varies across studies (different populations, methods, or settings) and estimates the average of that distribution of true effects, appropriate when heterogeneity is moderate to high. Choosing fixed-effects with high heterogeneity understates the true uncertainty in your pooled estimate.

Not sure whether your assignment needs a continuous- or binary-outcome meta-analysis? See the full SPSS statistical test guide, or get help with this specific assignment.