Bibliographic record
Abstract
Correspondence analysis is an exploratory data technique used to analyze categorical data (Benzecri, 1992).It is used in many areas such as marketing and ecology.Correspondence analysis has been used less often in psychological research, although it can be suitably applied.This article discusses the benefits of using correspondence analysis in psychological research and provides a tutorial on how to perform correspondence analysis using the Statistical Package for the Social Sciences (SPSS).Correspondence analysis (CA) has become most popular in fields such as ecology, where data is collected on the abundance of various animal species in specific sampling units/areas (terr Braak, 1985).The amount of data involved in these samples makes it difficult to get a clear sense of the data at a glance (Palmer, 1993).With the use of CA, an exploratory data technique for categorical data, ecologists have been able to transform these complicated tables into straightforward graphical displays (Hoffman & Franke, 1986).Ecological data is multidimensional making visualization of more than two dimensions difficult. Correspondence Analysis is an ideal technique to analyzethis form of data because of its ability to extract the most important dimensions, allowing simplification of the data matrix (Palmer).However, this statistical technique merits further attention within the field of psychological research.In fact, the lack of awareness of this useful statistical method puts psychological researchers at a disadvantage.To demonstrate that CA can be suitably applied to psychological research, this paper will use a research question from community psychology.Additionally, due to the gap in the literature on how to perform CA using statistical software programs, the current paper will describe how to perform CA using SPSS software.
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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 | 0.030 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".