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UNL PSYC 971 - Database Structures

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Database StructuresThe Hierarchical Nature of Meta-Analytic DataExample of a Flat Data FileAdvantages & Disadvantages of a Single Flat File Data StructureExample of Relational Data Structure (Multiple Related Flat Files)Example of a More Complex Multiple File Data StructureAdvantages & Disadvantages of Multiple Flat Files Data StructureConcept of “Working” Analysis FilesWhat about Sub-Samples?Coding Forms and Coding ManualDatabase Structure Overheads 1Database 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 2The 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 3Example of a Flat Data FileID Paradigm ES1 DV1 ES2 DV2 ES3 DV3 ES4 DV422 2 0.77 323 2 0.77 331 1 -0.1 5 -0.05 5 -0.2 1136 2 0.94 340 1 0.96 1182 1 0.29 11185 1 0.65 5 0.58 5 0.48 5 0.068 5186 1 0.83 5204 2 0.88 3229 2 0.97 3246 2 0.91 3274 2 0.86 3 -0.31 3 0.79 3 1.17 3295 2 7.03 3 6.46 3 . 3 0.57 .626 1 0.87 3 -0.04 3 0.1 3 0.9 31366 2 0.5 3Note that there is only one record (row) per study.Multiple ESs handled by having multiplevariables, one for each potential ES.Database Structure Overheads 4Advantages & 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 lowDatabase Structure Overheads 5Example of Relational Data Structure(Multiple Related Flat Files)ID PubYear MeanAge TxStyle100 92 15.5 27049 82 14.5 1OutcomeID ESNum Type TxN CgN ES100 1 1 24 24 -0.39100 2 1 24 24 0100 3 1 24 24 0.09100 4 1 24 24 -1.05100 5 1 24 24 -0.447049 1 2 30 30 0.347049 2 4 30 30 0.787049 3 1 30 30 0Note that a single record in the file above is “related” to five records in the file to the right.Study Level Data FileEffect Size Level Data FileDatabase Structure Overheads 6Example of a More Complex MultipleFile Data StructureID PubYear MeanAge TxStyle100 92 15.5 27049 82 14.5 1Study Level Data File Outcome Level Data FileID OutNum Constrct Scale100 1 2 1100 2 6 1100 3 4 27049 1 2 47049 2 6 3ID OutNum ESNum Months TxN CgN ES100 1 1 0 24 24 -0.39100 1 2 6 22 22 0100 2 3 0 24 24 0.09100 2 4 6 22 22 -1.05100 3 5 0 24 24 -0.44100 3 6 6 22 21 0.347049 1 2 0 30 30 0.787049 1 6 12 29 28 0.787049 2 2 0 30 30 0Effect Size Level Data FileNote 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 7Advantages & 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 possibleDatabase Structure Overheads 8Concept of “Working” Analysis FilesStudy Data FileOutcome Data FileES Data FileComposite Data Filecreatecompositedata fileselect subset of ESs of interest to current analysis,e.g., a specific outcome atposttestverify that there is only asingle ES per studyyesWorking Analysis FilePermanent Data FilesAverage ESs, further selectbased explicit criteria, orselect randomlynoDatabase Structure Overheads 9What 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 fileDatabase Structure Overheads 10Coding 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


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