The NeuroPsychNorms database contains normative data from peer-reviewed studies replaced to more the 50 measures. Overall, more than 1000+ studies on the different measures were input.
In addition to normative data added by the NeuroPsychNorms team to the database, you are more than welcome to suggest studies containing new uncollected data or even measures for which we do not have any data at all.
While our literature tended to focus on normative data and/or control subjects on an experimental trial, the database also includes abnormal data (e.g., TBI, dementia, stroke) and unique data (e.g., re-test data and under other unique conditions). Again, feel free to suggest new studies with abnorms and unique conditions to an existing measure.
The database is configured so you can sort the data easily by the N-size, Demographics or Year. Number of Demographic Stratifications is determined by the number of Stratification types that the study separates their data based on demographic information. For example, if the data of a study is separated by Age, Education, and Gender Identity, this will be at a higher rank than a 2-tier stratification (Age and Education).
After comprehensively searching the neuropsychological literature, we
determined which measures we deemed important to include normative data
for the database.
We sought out the most commonly neuropsychological measures currently used
in both clinical and research settings. We made an effort to focus on
cross-cultural neuropsychological measures utilized in the
neuropsychological literature, due to the scarcity of these
Normative data provided by NeuroPsychNorms are collected from peer-reviewed studies.
After comprehensively looking at each measure we selected, we determined which raw data is commonly scored throughout the literature. For example, the Trail Making Test commonly utilizes scores for Trial A and B while some studies provide B-A, or B+A, B-A/B and errors. We did not include those specific measures in the database.
In order for the scoring program to function, the studies must provide means and standard deviations of the measure's variables. As a consequence, we excluded from our database studies which do not include both a Mean and Standard Deviation of a variable.
If the normative data was not stratified (based on demographics, such as age, education, and gender identity), which is common for control studies comparing an experimental group, an age range was at minimum required to be included in the database. While age was required, we find that education to be important but not required. We included those studies even if they did not report any information on education. Some studies gave also information such as a ratio of males to females but did not actually separate their data based on this gender identity demographic. Stratified data includes gender identity stratification only if the study actually provided separate scores based on gender.
Number of Demographic Stratifications is determined by the number of Stratification types that the study separates their data based on demographic information. The most common types are age, gender identity, and education. For example, if the data of a study is separated by these three demographic information, the study will have a higher rank than a 2-tier stratification. The program has the capacity to rank based on the number of stratifications by selecting the Demographics button next to the Enter button.
Scored data are presented by providing a z-score and precentiles only, at this time.