Database Structures

• The hierarchical nature of meta-analytic data • The familiar flat data file • The relational data file • Advantages and disadvantages of each • What about the meta-analysis bibliography?

Database Structure Overheads 1

The Hierarchical Nature of Meta-Analytic Data

• Meta-analytic data is inherently hierarchical – Multiple outcomes per study – Multiple measurement points per study – Multiple sub-samples per study – Results in multiple effect sizes per study • Any specific analysis can only include one effect size per study (or one effect size per sub-sample within a study) • Analyses almost always are of a subset of coded effect sizes. Data structure needs to allow for the selection and creation of those subset.

Database Structure Overheads 2

Example of a Flat Data File

Multiple ESs handled by having multiple variables, one for each potential ES.

185 186 204 229 246 274

ID

22 23 31 36 40 82 295 626 1366

Paradigm

2 2 1 2 1 1 2 1 2 1 1 2 2 2 2

ES1

0.77

0.77

-0.1

0.94

0.96

0.29

0.65

0.97

0.86

7.03

0.87

DV1

3 3 5 3 11 11 5 3 3 3 3

ES2

-0.05

0.58

0.83

0.88

0.91

-0.31

6.46

-0.04

0.5

DV2

5 5 5 3 3 3 3 .

3 3

ES3

0.48

0.79

0.1

Note that there is only one record (row) per study.

DV3

5 3 3 3 Database Structure Overheads

ES4 DV4

-0.2

0.068

1.17

0.57 .

0.9

11 5 3 3 3

Advantages & Disadvantages of a Single Flat File Data Structure

• Advantages – All data is stored in a single location – Familiar and easy to work with – No manipulation of data files prior to analysis • Disadvantages – Only a limited number of ESs can be calculated per study – Any adjustments applied to ESs must be done repeatedly • When to use – Interested in a small predetermined set of ESs – Number of coded variables is modest – Comfort level with a multiple data file structure is low Database Structure Overheads 4

Example of Relational Data Structure (Multiple Related Flat Files)

Study Level Data File ID 100 7049 PubYear 92 82 MeanAge 15.5

14.5

TxStyle 2 1 Note that a single record in the file above is “related” to five records in the file to the right.

Effect Size Level Data File ID 100 100 100 100 100 7049 7049 7049 ESNum 1 2 3 4 5 1 2 3 Outcome Type 1 1 1 1 1 2 4 1 TxN 24 24 24 24 24 30 30 30 CgN 24 24 24 24 24 30 30 30 ES -0.39

0 0.09

-1.05

-0.44

0.34

0.78

0 Database Structure Overheads 5

Example of a More Complex Multiple File Data Structure

Study Level Data File ID 100 7049 PubYear 92 82 MeanAge 15.5

14.5

TxStyle 2 1 Outcome Level Data File ID 100 100 100 7049 7049 OutNum 1 2 3 1 2 Constrct 2 6 4 2 6 Scale 1 1 2 4 3 ID 100 100 100 100 100 100 7049 7049 7049 Effect Size Level Data File OutNum 1 1 2 2 3 3 1 1 2 ESNum 1 2 3 4 5 6 2 6 2 Months 0 6 0 6 0 6 0 12 0 TxN 24 22 24 22 24 22 30 29 30 CgN ES 24 -0.39

22 24 0 0.09

22 -1.05

24 -0.44

21 30 0.34

0.78

28 30 0.78

0 Note that study 100 has 2 records in the outcomes data file and 6 outcomes in the effect size data file, 2 for each outcome measured at different points in time (Months).

Database Structure Overheads 6

Advantages & Disadvantages of Multiple Flat Files Data Structure

• Advantages – Can “grow” to any number of ESs – Reduces coding task (faster coding) – Simplifies data cleanup – Smaller data files to manipulate • Disadvantages – Complex to implement – Data must be manipulated prior to analysis (creation of “working” analysis files) – Must be able to select a single ES per study for any given analysis.

• When to use – Large number of ESs per study are possible Database Structure Overheads 7

Concept of “Working” Analysis Files

Permanent Data Files Study Data File Outcome Data File ES Data File create composite data file Composite Data File select subset of ESs of interest to current analysis, e.g., a specific outcome at posttest verify that there is only a single ES per study yes no Average ESs, further select based explicit criteria, or select randomly Working Analysis File Database Structure Overheads 8

What about Sub-Samples?

• So far I have assumed that the only ESs that have been coded were based on the full study sample.

• What if you are interested in coding ESs separately for different sub-samples, such as, boys and girls, or high-risk and low-risk youth, etc?

– Just say “no”!

• Often not enough of such data for meaningful analysis • Complicates coding and data structure – Well, if you must, plan your data structure carefully • Include a full sample effect size for each dependent measure of interest • Place sub-sample in a separate data file Database Structure Overheads 9

Coding Forms and Coding Manual

• Paper Coding (see Appendix E) – include data file variable names on coding form – all data along left or right margin eases data entry • Coding Directly into a Computer Database Database Structure Overheads 10