Formatting data files for repeated-measures analyses in SPSS: Using the Aggregate and Restructure procedures
Bibliographic record
Abstract
In this tutorial, we demonstrate how to use the Aggregate and Restructure procedures available in SPSS (versions 11 and up) to prepare data files for repeated-measures analyses.In the first two sections of the tutorial, we briefly describe the Aggregate and Restructure procedures.In the final section, we present an example in which the data from a fictional lexical decision task are prepared for analysis using a mixed-design ANOVA.The tutorial demonstrates that the presented method is the most efficient way to prepare data for repeated-measures analyses in SPSS.Specialized experiment generation and data collection software has greatly simplified the experimental psychologist's work during the last few decades.Commercially and freely available software such as E-Prime (Schneider, Eschman, & Zuccolotto, 2002a; 2002b) and PsyScope (Cohen, MacWhinney, Flatt, & Provost, 1993) allow for the creation of basic experiments in a few minutes.Moreover, once the testing is finished, the experimenter possesses a complete digital record of the participants' responses and reaction times, which can then be submitted to statistical analyses.Unfortunately, the raw data output files are rarely in a format to be immediately analyzed with SPSS.Because the typical data file usually includes one line per experimental trial (with participant identification, independent variables, and dependent variables appearing in separate columns), analyses involving repeated measures cannot be conducted without additional work.One must first calculate the average performance for each dependent variable at each level of each condition.Then, the data file must be reconfigured to show each participant's data on a single row (See Figure 1 for an illustration of the steps to be taken).It is only when these steps have been completed that the
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.064 | 0.033 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".