Abstract:
:Developmental studies of hormones and behavior often include littermates-rodent siblings that share early-life experiences and genes. Due to between-litter variation (i.e., litter effects), the statistical assumption of independent observations is untenable. In two literatures-natural variation in maternal care and prenatal stress-entire litters are categorized based on maternal behavior or experimental condition. Here, we (1) review both literatures; (2) simulate false positive rates for commonly used statistical methods in each literature; and (3) characterize small sample performance of multilevel models (MLM) and generalized estimating equations (GEE). We found that the assumption of independence was routinely violated (>85%), false positives (α=0.05) exceeded nominal levels (up to 0.70), and power (1-β) rarely surpassed 0.80 (even for optimistic sample and effect sizes). Additionally, we show that MLMs and GEEs have adequate performance for common research designs. We discuss implications for the extant literature, the field of behavioral neuroendocrinology, and provide recommendations.
journal_name
Front Neuroendocrinoljournal_title
Frontiers in neuroendocrinologyauthors
Williams DR,Carlsson R,Bürkner PCdoi
10.1016/j.yfrne.2017.08.003subject
Has Abstractpub_date
2017-10-01 00:00:00pages
154-166eissn
0091-3022issn
1095-6808pii
S0091-3022(17)30046-8journal_volume
47pub_type
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