Abstract
Psychological tests and questionnaires are often used to assess a person’s cognitive, emotional, and behavioral functioning. The large majority of psychological tests are norm-referenced. This means that normative data are needed to allow for a meaningful interpretation of the test scores. For example, knowing that a person answered 20 out of 50 items correctly on a test of abstract reasoning is not informative by itself. To give this test score a meaningful interpretation, the relative position of the score in a broader reference group (i.e., a normative sample) should be known.
Traditional normative data consist of subgroup-specific summary statistics of the test scores in a normative sample. This normative approach is straightforward, but it has some fundamental limitations. For example, it is difficult to derive fine-grained norms that account for the impact of several independent variables (such as Age, Gender, and Level of Education). Indeed, the subgroups become small when multiple independent variables have to be accounted for, which results in imprecise norms. The regression-based normative approach provides a statistically principled alternative to the traditional normative method that is substantially less hampered by such issues.
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Notes
- 1.
Often the normative sample is drawn from a subset of the normative population. For example, the normative population for a verbal memory test like the VLT will typically exclude people who have severe cognitive disorders (e.g., people who are diagnosed with Alzheimer’s disease or frontotemporal dementia). The reason for this is that one is often primarily interested in obtaining reference data that reflect the population distribution of the test scores for “typical” test participants who have “normal” ability levels in a normative data context.
- 2.
These are simulated data. The population distribution of the test scores is never known in real-life normative studies, because it is not feasible to test the entire population. For some tests, a large proportion of the normative population can be tested (e.g., for standardized school tests), but even then the entire population distribution is not known due to missing data (e.g., children who were not tested due to illness).
- 3.
In Appendix A.1, a comprehensive table of \(\widehat {\upsilon }_0\)-scores and their corresponding \(\widehat {\pi }_0\) is provided.
- 4.
In a real-life normative analysis, a formal statistical test is typically conducted to decide whether an independent variable (like Gender) should be accounted for in the normative data (see subsequent chapters). It is assumed here that Gender has a statistically significant impact on the mean VLT Total Recall score.
- 5.
In the notation that is used for the Age subgroup intervals, a round bracket (i.e., the “(”-symbol) indicates that the specified value is not included in the interval, whereas a square bracket (i.e., the “]”-symbol) indicates that the specified value is included in the interval. For example, the Age subgroup \((20.00, \: 30.00]\) contains test participants who are aged \({>}20.00\) years and \({\leq }30.00\) years.
- 6.
The standard error of an estimated parameter like the mean or the SD reflects the uncertainty in the estimated values (i.e., the sample-to-sample variability, see Chap. 3). When the standard error increases, the precision of the estimated summary statistic decreases.
- 7.
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Van der Elst, W. (2024). General Introduction. In: Regression-Based Normative Data for Psychological Assessment. Springer, Cham. https://doi.org/10.1007/978-3-031-50951-3_1
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