Meta-Analysis Programs & Datasets

Field, A. P. & Gillett, R. (2010). How to do a meta-analysis. British Journal of Mathematical and Statistical Psychology, 63, 665-694.


Getting Started

This website contains the files associated with Field, A. P. & Gillett, R. (2010). How to do meta-analysis. British Journal of Mathematical and Statistical Psychology, 63, 665-694. Please see this article for a full description of the files and how to use them.

The programs are SPSS command syntax files (written for IBM SPSS 16.0 or later) and R programs (written for R 2.7.2 or later).  The datasets are SPSS data files based on published meta-analyses in the field of psychology.

  1. Create a new folder Meta-Analysis in the Documents folder of your PC/Mac. (In Windows 7 this folder appears as 'My Documents' in explorer but is called 'Documents', in versions of Windows older than Vista it is actually called 'My Documents' and you will need to edit the syntax files accordingly - see below).
  2. Download the programs and datasets on this webpage to the Meta-Analysis folder
  3. The setup program for R may be downloaded from http://www.stats.bris.ac.uk/R/  (for Windows and Mac)
  4. R should be installed on the same PC/Mac as SPSS
  5. Familiarity with R is not required to run the programs

IMPORTANT: Many of these syntax files have a command in them that changes the directory to be your 'Meta-Analysis' directory. This line of code will need to be changed depending on your operating system:


Basic Meta-Analysis

Each program for basic meta-analysis provides:
  1. An estimate of the average effect size of all studies, or any subset of studies
  2. A test of homogeneity of effect size that contributes to the assessment of the goodness of fit of the statistical model
  3. Elementary indicators of, and tests for, the presence of publication bias
  4. Output for both fixed-effects and random-effects models
Effect-Size MeasureSymbolProgram Dataset What Your Output should Look Like
CorrelationrMeta_Basic_r.sps Cartwright-Hatton_et_al_2004.sav
Cartwright-Hatton SPSS Output
Correlation (Hunter Schmidt Only) r h_s syntax.sps    
Standardised Difference
Between Two Means
dMeta_Basic_d.sps Else-Quest_et_al_2006.sav
Else-Quest SPSS Output
Difference Between
Two Proportions
D or hMeta_Basic_D_h.sps Pozzulo_&_Lindsay_1998.sav
Pozzulo SPSS Output

Example 1: Correlation

Example 2: Correlation (Hunter Schmidt Only)

Example 3: Standardised Difference Between Two Means Example 4: Difference Between Two Proportions

Moderator Variable Analysis

A moderator variable is a predictor variable, with a known value for each study, that has been proposed (preferably on theoretical grounds) as a candidate to explain heterogeneity among effect sizes.

The programs for analysing the influence of moderator variables use weighted multiple regression to supply:

  1. An evaluation of the impact of continuous moderator variables on effect size
  2. An evaluation of the impact of categorical moderator variables on effect size
  3. Tests of homogeneity of effect size that contribute to the assessment of the goodness of fit of a statistical model incorporating a given set of moderator variables
  4. Output for both fixed-effects and random-effects models

Each program is run by means of an individual launcher program which minimises the need for user input and provides a template to facilitate customisation.

Effect-Size MeasureSymbolProgram LauncherDataset
Correlationr Meta_Mod_r.sps Launch_Meta_Mod_r.sps Tenenbaum_&_Leaper_2002.sav
Standardised Difference
Between Two Means
d Meta_Mod_d.sps Launch_Meta_Mod_d.sps Else-Quest_et_al_2006.sav
Difference Between
Two Proportions
D or h Meta_Mod_D_h.sps Launch_Meta_Mod_D_h.sps Pozzulo_&_Lindsay_1998.sav

Coding Categorical Moderators
A categorical variable with  c  categories should be coded using the first  c  integers, 1, 2, ..., c,  to denote the categories.

Example 4: Correlation Example 5: Standardised Difference Between Two Means Example 6: Difference Between Two Proportions

Sensitivity of Effect-Size Estimates to Publication Bias

To supplement the elementary tests for publication bias described above, programs for producing funnel plots and assessing the vulnerability of effect-size estimates to publication bias are provided. They are written in R and have been adapted from Vevea & Woods (2005). The programs employ sensitivity analysis to investigate the impact of moderate and severe levels of bias. Effect-size estimates may be considered robust to the extent that they would be relatively unaffected were a substantial level of bias to be present.

Each program supplies:

  1. An ordinary unadjusted estimate of effect size
  2. Four adjusted estimates of effect size that indicate the potential impact of
  3. Output for both fixed-effects and random-effects models

R should be installed on the same PC/Mac as SPSS, as described in Getting Started. Notive that ythere are Windows and Mac versions of the files, the only difference is one line of code that 'finds' the publication bias data from your earlier analysis. On Windows the codeline is

fp <- paste(Sys.getenv("R_USER"),"Meta-Analysis","Pub_Bias_Data.sav",sep="\\")

but on a Mac (MacOS 10) it needs to be

fp <- paste(Sys.getenv("HOME"),"/Documents/Meta-Analysis","Pub_Bias_Data.sav",sep="/")

The different files differ only in this line of code. You can edit this line of code to look for the file "Pub_Bias_Data.sav"if you saved it somewhere other than ~/Documents/Meta-Analysis/

Effect-Size MeasureSymbolProgramDataset
Correlationr

Pub_Bias_r.R (Windows)

Pub_Bias_r.R (Mac)

Cartwright-Hatton_et_al_2004.sav
Standardised Difference
Between Two Means
d

Pub_Bias_d.R (Windows)

Pub_Bias_d.R (Mac)

Else-Quest_et_al_2006.sav
Difference Between
Two Proportions
D or h

Pub_Bias_D_h.R (Windows)

Pub_Bias_D_h.R (Mac)

Pozzulo_&_Lindsay_1998.sav

Example 7: Correlation

Example 8: Standardised Difference Between Two Means

Example 9: Difference Between Two Proportions

Links related to Meta-Analysis or our Article

Web page by Andy Field and Raphael Gillett, 2009, Last Updated September 2010