Developmental psychologists should adopt citizen science to improve generalization and reproducibility.

Widespread failures of replication and generalization are, ironically, a scientific triumph, in that they confirm the fundamental metascientific theory that underlies our field. Generalizable and replicable findings require testing large numbers of subjects from a wide range of demographics with a large, randomly-sampled stimulus set, and using a variety of experimental parameters. Because few studies accomplish any of this, meta-scientists predict that findings will frequently fail to replicate or generalize. We argue that to be more robust and replicable, developmental psychology needs to find a mechanism for collecting data at greater scale and from more diverse populations. Luckily, this mechanism already exists: Citizen science, in which large numbers of uncompensated volunteers provide data. While best-known for its contributions to astronomy and ecology, citizen science has also produced major findings in neuroscience and psychology, and increasingly in developmental psychology. We provide examples, address practical challenges, discuss limitations, and compare to other methods of obtaining large datasets. Ultimately, we argue that the range of studies where it makes sense *not* to use citizen science is steadily dwindling.

Establishing construct validity of neuropsychological tests in cancer survivors.

OBJECTIVE: Cancer-related cognitive impairments (CRCI) are frequently reported among cancer survivors, and attention is the most frequently assessed cognitive domain in CRCI. However, there is no consensus as to whether attention is impaired. We suggest that a major reason for this lack of agreement is a lack of construct validity for neuropsychological attention tests. We propose to assess the construct validity of neuropsychological attention tests with respect to experimental paradigms from cognitive psychology. METHODS: Self-reported cancer survivors (N = 314) completed an online battery comprising six experimental attention paradigms and eight neuropsychological tests. Confirmatory factor analysis was used to evaluate the fit of five models derived from a general population sample (N = 636) in a previous study (M. Treviño, Cogn Res Princ Implic, in press). We then subjected the best-fitting model to a measurement invariance analysis. RESULTS: The best-fitting model was a six intercorrelated factor structure, comprising Capacity, Search, Digit Span, Arithmetic, Sustained Attention, and Flanker Interference factors. Configural and weak invariance held, indicating that the factor loadings were invariant across groups. Strong invariance, indicating that intercepts were also invariant, held except for the Approximate Number Sense test. CONCLUSIONS: According to our factor model, Spatial Span and Digit Symbol Coding measure attentional capacity, while the Trail Making Test (A&B) and Letter Cancellation tests measure visual search ability. However, Digit Span and Arithmetic tests do not measure attention. We hope that these results will lead to better scientific models, better patient education, and, ultimately, improved outcomes for survivors.